example of a speech recognition

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Speech Recognition examples with Python

Speech recognition technologies have experienced immense advancements, allowing users to convert spoken language into textual data effortlessly. Python, a versatile programming language, boasts an array of libraries specifically tailored for speech recognition. Notable among these are the Google Speech Engine, Google Cloud Speech API, Microsoft Bing Voice Recognition, and IBM Speech to Text.

In this tutorial, we’ll explore how to seamlessly integrate Python with Google’s Speech Recognition Engine.

Setting Up the Development Environment

To embark on your speech recognition journey with Python, it’s imperative to equip your development environment with the right tools. The SpeechRecognition library streamlines this preparation. Whether you’re accustomed to using pyenv, pipenv, or virtualenv, this library is seamlessly compatible. For a global installation, input the command below:

It’s vital to acknowledge that the SpeechRecognition library hinges on pyaudio. The installation methodology for pyaudio might differ based on the operating system. For example, Manjaro Linux users can find packages labeled “python-pyaudio” and “python2-pyaudio”. Always ensure that you’ve selected the appropriate package tailored for your OS.

Introduction to Speech Recognition

Are you eager to test the prowess of the speech recognition module firsthand? Simply run the command given below and watch the magic unfold within your terminal:

A Closer Look at Google’s Speech Recognition Engine

Now, let’s delve deeper into the capabilities of Google’s renowned Speech Recognition engine. While this tutorial predominantly showcases its usage in English, it’s noteworthy that the engine is proficient in handling a plethora of languages.

A vital point to consider: this walkthrough employs the standard Google API key. If you’re inclined to integrate an alternate API key, modify the code accordingly:

Eager to witness this engine in action? Incorporate the code detailed below, save the file as “speechtest.py”, and initiate the script using Python 3:

Dive Deeper with Downloadable Speech Recognition Samples

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It works quite well for me

It doesn't run for me it just prints on the console "speak:" and doesn't do anything else

Check which microphone inputs are available and set the right input. Also try with different speech engines https://github.com/Uberi/sp...

How can I change the microphone settings? it doesn't seem to pick it up for me, mic is attached via USB

Make sure you have PyAudio installed. Then you can change the id of the microphone like so: Microphone(device_index=3) . Change the device_index to the microphone id you need. You can list the microphones this way:

import speech_recognition as sr for index, name in enumerate(sr.Microphone.list_microphone_names()): print('Microphone with name "{1}" found for `Microphone(device_index={0})`'.format(index, name))

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The Ultimate Guide To Speech Recognition With Python

Table of Contents

How Speech Recognition Works – An Overview

Picking a python speech recognition package, installing speechrecognition, the recognizer class, supported file types, using record() to capture data from a file, capturing segments with offset and duration, the effect of noise on speech recognition, installing pyaudio, the microphone class, using listen() to capture microphone input, handling unrecognizable speech, putting it all together: a “guess the word” game, recap and additional resources, appendix: recognizing speech in languages other than english.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Speech Recognition With Python

Have you ever wondered how to add speech recognition to your Python project? If so, then keep reading! It’s easier than you might think.

Far from a being a fad, the overwhelming success of speech-enabled products like Amazon Alexa has proven that some degree of speech support will be an essential aspect of household tech for the foreseeable future. If you think about it, the reasons why are pretty obvious. Incorporating speech recognition into your Python application offers a level of interactivity and accessibility that few technologies can match.

The accessibility improvements alone are worth considering. Speech recognition allows the elderly and the physically and visually impaired to interact with state-of-the-art products and services quickly and naturally—no GUI needed!

Best of all, including speech recognition in a Python project is really simple. In this guide, you’ll find out how. You’ll learn:

• How speech recognition works,
• What packages are available on PyPI; and
• How to install and use the SpeechRecognition package—a full-featured and easy-to-use Python speech recognition library.

In the end, you’ll apply what you’ve learned to a simple “Guess the Word” game and see how it all comes together.

Free Bonus: Click here to download a Python speech recognition sample project with full source code that you can use as a basis for your own speech recognition apps.

Before we get to the nitty-gritty of doing speech recognition in Python, let’s take a moment to talk about how speech recognition works. A full discussion would fill a book, so I won’t bore you with all of the technical details here. In fact, this section is not pre-requisite to the rest of the tutorial. If you’d like to get straight to the point, then feel free to skip ahead.

Speech recognition has its roots in research done at Bell Labs in the early 1950s. Early systems were limited to a single speaker and had limited vocabularies of about a dozen words. Modern speech recognition systems have come a long way since their ancient counterparts. They can recognize speech from multiple speakers and have enormous vocabularies in numerous languages.

The first component of speech recognition is, of course, speech. Speech must be converted from physical sound to an electrical signal with a microphone, and then to digital data with an analog-to-digital converter. Once digitized, several models can be used to transcribe the audio to text.

Most modern speech recognition systems rely on what is known as a Hidden Markov Model (HMM). This approach works on the assumption that a speech signal, when viewed on a short enough timescale (say, ten milliseconds), can be reasonably approximated as a stationary process—that is, a process in which statistical properties do not change over time.

In a typical HMM, the speech signal is divided into 10-millisecond fragments. The power spectrum of each fragment, which is essentially a plot of the signal’s power as a function of frequency, is mapped to a vector of real numbers known as cepstral coefficients. The dimension of this vector is usually small—sometimes as low as 10, although more accurate systems may have dimension 32 or more. The final output of the HMM is a sequence of these vectors.

To decode the speech into text, groups of vectors are matched to one or more phonemes —a fundamental unit of speech. This calculation requires training, since the sound of a phoneme varies from speaker to speaker, and even varies from one utterance to another by the same speaker. A special algorithm is then applied to determine the most likely word (or words) that produce the given sequence of phonemes.

One can imagine that this whole process may be computationally expensive. In many modern speech recognition systems, neural networks are used to simplify the speech signal using techniques for feature transformation and dimensionality reduction before HMM recognition. Voice activity detectors (VADs) are also used to reduce an audio signal to only the portions that are likely to contain speech. This prevents the recognizer from wasting time analyzing unnecessary parts of the signal.

Fortunately, as a Python programmer, you don’t have to worry about any of this. A number of speech recognition services are available for use online through an API, and many of these services offer Python SDKs .

A handful of packages for speech recognition exist on PyPI. A few of them include:

• google-cloud-speech
• pocketsphinx
• SpeechRecognition
• watson-developer-cloud

Some of these packages—such as wit and apiai—offer built-in features, like natural language processing for identifying a speaker’s intent, which go beyond basic speech recognition. Others, like google-cloud-speech, focus solely on speech-to-text conversion.

There is one package that stands out in terms of ease-of-use: SpeechRecognition.

Recognizing speech requires audio input, and SpeechRecognition makes retrieving this input really easy. Instead of having to build scripts for accessing microphones and processing audio files from scratch, SpeechRecognition will have you up and running in just a few minutes.

The SpeechRecognition library acts as a wrapper for several popular speech APIs and is thus extremely flexible. One of these—the Google Web Speech API—supports a default API key that is hard-coded into the SpeechRecognition library. That means you can get off your feet without having to sign up for a service.

The flexibility and ease-of-use of the SpeechRecognition package make it an excellent choice for any Python project. However, support for every feature of each API it wraps is not guaranteed. You will need to spend some time researching the available options to find out if SpeechRecognition will work in your particular case.

So, now that you’re convinced you should try out SpeechRecognition, the next step is getting it installed in your environment.

SpeechRecognition is compatible with Python 2.6, 2.7 and 3.3+, but requires some additional installation steps for Python 2 . For this tutorial, I’ll assume you are using Python 3.3+.

You can install SpeechRecognition from a terminal with pip:

Once installed, you should verify the installation by opening an interpreter session and typing:

Note: The version number you get might vary. Version 3.8.1 was the latest at the time of writing.

Go ahead and keep this session open. You’ll start to work with it in just a bit.

SpeechRecognition will work out of the box if all you need to do is work with existing audio files. Specific use cases, however, require a few dependencies. Notably, the PyAudio package is needed for capturing microphone input.

You’ll see which dependencies you need as you read further. For now, let’s dive in and explore the basics of the package.

All of the magic in SpeechRecognition happens with the Recognizer class.

The primary purpose of a Recognizer instance is, of course, to recognize speech. Each instance comes with a variety of settings and functionality for recognizing speech from an audio source.

Creating a Recognizer instance is easy. In your current interpreter session, just type:

Each Recognizer instance has seven methods for recognizing speech from an audio source using various APIs. These are:

• recognize_bing() : Microsoft Bing Speech
• recognize_google() : Google Web Speech API
• recognize_google_cloud() : Google Cloud Speech - requires installation of the google-cloud-speech package
• recognize_houndify() : Houndify by SoundHound
• recognize_ibm() : IBM Speech to Text
• recognize_sphinx() : CMU Sphinx - requires installing PocketSphinx
• recognize_wit() : Wit.ai

Of the seven, only recognize_sphinx() works offline with the CMU Sphinx engine. The other six all require an internet connection.

A full discussion of the features and benefits of each API is beyond the scope of this tutorial. Since SpeechRecognition ships with a default API key for the Google Web Speech API, you can get started with it right away. For this reason, we’ll use the Web Speech API in this guide. The other six APIs all require authentication with either an API key or a username/password combination. For more information, consult the SpeechRecognition docs .

Caution: The default key provided by SpeechRecognition is for testing purposes only, and Google may revoke it at any time . It is not a good idea to use the Google Web Speech API in production. Even with a valid API key, you’ll be limited to only 50 requests per day, and there is no way to raise this quota . Fortunately, SpeechRecognition’s interface is nearly identical for each API, so what you learn today will be easy to translate to a real-world project.

Each recognize_*() method will throw a speech_recognition.RequestError exception if the API is unreachable. For recognize_sphinx() , this could happen as the result of a missing, corrupt or incompatible Sphinx installation. For the other six methods, RequestError may be thrown if quota limits are met, the server is unavailable, or there is no internet connection.

Ok, enough chit-chat. Let’s get our hands dirty. Go ahead and try to call recognize_google() in your interpreter session.

What happened?

You probably got something that looks like this:

You might have guessed this would happen. How could something be recognized from nothing?

All seven recognize_*() methods of the Recognizer class require an audio_data argument. In each case, audio_data must be an instance of SpeechRecognition’s AudioData class.

There are two ways to create an AudioData instance: from an audio file or audio recorded by a microphone. Audio files are a little easier to get started with, so let’s take a look at that first.

Working With Audio Files

Before you continue, you’ll need to download an audio file. The one I used to get started, “harvard.wav,” can be found here . Make sure you save it to the same directory in which your Python interpreter session is running.

SpeechRecognition makes working with audio files easy thanks to its handy AudioFile class. This class can be initialized with the path to an audio file and provides a context manager interface for reading and working with the file’s contents.

Currently, SpeechRecognition supports the following file formats:

• WAV: must be in PCM/LPCM format
• FLAC: must be native FLAC format; OGG-FLAC is not supported

If you are working on x-86 based Linux, macOS or Windows, you should be able to work with FLAC files without a problem. On other platforms, you will need to install a FLAC encoder and ensure you have access to the flac command line tool. You can find more information here if this applies to you.

Type the following into your interpreter session to process the contents of the “harvard.wav” file:

The context manager opens the file and reads its contents, storing the data in an AudioFile instance called source. Then the record() method records the data from the entire file into an AudioData instance. You can confirm this by checking the type of audio :

You can now invoke recognize_google() to attempt to recognize any speech in the audio. Depending on your internet connection speed, you may have to wait several seconds before seeing the result.

Congratulations! You’ve just transcribed your first audio file!

If you’re wondering where the phrases in the “harvard.wav” file come from, they are examples of Harvard Sentences. These phrases were published by the IEEE in 1965 for use in speech intelligibility testing of telephone lines. They are still used in VoIP and cellular testing today.

The Harvard Sentences are comprised of 72 lists of ten phrases. You can find freely available recordings of these phrases on the Open Speech Repository website. Recordings are available in English, Mandarin Chinese, French, and Hindi. They provide an excellent source of free material for testing your code.

What if you only want to capture a portion of the speech in a file? The record() method accepts a duration keyword argument that stops the recording after a specified number of seconds.

For example, the following captures any speech in the first four seconds of the file:

The record() method, when used inside a with block, always moves ahead in the file stream. This means that if you record once for four seconds and then record again for four seconds, the second time returns the four seconds of audio after the first four seconds.

Notice that audio2 contains a portion of the third phrase in the file. When specifying a duration, the recording might stop mid-phrase—or even mid-word—which can hurt the accuracy of the transcription. More on this in a bit.

In addition to specifying a recording duration, the record() method can be given a specific starting point using the offset keyword argument. This value represents the number of seconds from the beginning of the file to ignore before starting to record.

To capture only the second phrase in the file, you could start with an offset of four seconds and record for, say, three seconds.

The offset and duration keyword arguments are useful for segmenting an audio file if you have prior knowledge of the structure of the speech in the file. However, using them hastily can result in poor transcriptions. To see this effect, try the following in your interpreter:

By starting the recording at 4.7 seconds, you miss the “it t” portion a the beginning of the phrase “it takes heat to bring out the odor,” so the API only got “akes heat,” which it matched to “Mesquite.”

Similarly, at the end of the recording, you captured “a co,” which is the beginning of the third phrase “a cold dip restores health and zest.” This was matched to “Aiko” by the API.

There is another reason you may get inaccurate transcriptions. Noise! The above examples worked well because the audio file is reasonably clean. In the real world, unless you have the opportunity to process audio files beforehand, you can not expect the audio to be noise-free.

Noise is a fact of life. All audio recordings have some degree of noise in them, and un-handled noise can wreck the accuracy of speech recognition apps.

To get a feel for how noise can affect speech recognition, download the “jackhammer.wav” file here . As always, make sure you save this to your interpreter session’s working directory.

This file has the phrase “the stale smell of old beer lingers” spoken with a loud jackhammer in the background.

What happens when you try to transcribe this file?

So how do you deal with this? One thing you can try is using the adjust_for_ambient_noise() method of the Recognizer class.

That got you a little closer to the actual phrase, but it still isn’t perfect. Also, “the” is missing from the beginning of the phrase. Why is that?

The adjust_for_ambient_noise() method reads the first second of the file stream and calibrates the recognizer to the noise level of the audio. Hence, that portion of the stream is consumed before you call record() to capture the data.

You can adjust the time-frame that adjust_for_ambient_noise() uses for analysis with the duration keyword argument. This argument takes a numerical value in seconds and is set to 1 by default. Try lowering this value to 0.5.

Well, that got you “the” at the beginning of the phrase, but now you have some new issues! Sometimes it isn’t possible to remove the effect of the noise—the signal is just too noisy to be dealt with successfully. That’s the case with this file.

If you find yourself running up against these issues frequently, you may have to resort to some pre-processing of the audio. This can be done with audio editing software or a Python package (such as SciPy ) that can apply filters to the files. A detailed discussion of this is beyond the scope of this tutorial—check out Allen Downey’s Think DSP book if you are interested. For now, just be aware that ambient noise in an audio file can cause problems and must be addressed in order to maximize the accuracy of speech recognition.

When working with noisy files, it can be helpful to see the actual API response. Most APIs return a JSON string containing many possible transcriptions. The recognize_google() method will always return the most likely transcription unless you force it to give you the full response.

You can do this by setting the show_all keyword argument of the recognize_google() method to True.

As you can see, recognize_google() returns a dictionary with the key 'alternative' that points to a list of possible transcripts. The structure of this response may vary from API to API and is mainly useful for debugging.

By now, you have a pretty good idea of the basics of the SpeechRecognition package. You’ve seen how to create an AudioFile instance from an audio file and use the record() method to capture data from the file. You learned how to record segments of a file using the offset and duration keyword arguments of record() , and you experienced the detrimental effect noise can have on transcription accuracy.

Now for the fun part. Let’s transition from transcribing static audio files to making your project interactive by accepting input from a microphone.

Working With Microphones

To access your microphone with SpeechRecognizer, you’ll have to install the PyAudio package . Go ahead and close your current interpreter session, and let’s do that.

The process for installing PyAudio will vary depending on your operating system.

Debian Linux

If you’re on Debian-based Linux (like Ubuntu) you can install PyAudio with apt :

Once installed, you may still need to run pip install pyaudio , especially if you are working in a virtual environment .

For macOS, first you will need to install PortAudio with Homebrew, and then install PyAudio with pip :

On Windows, you can install PyAudio with pip :

Testing the Installation

Once you’ve got PyAudio installed, you can test the installation from the console.

Make sure your default microphone is on and unmuted. If the installation worked, you should see something like this:

Shell A moment of silence, please... Set minimum energy threshold to 600.4452854381937 Say something! Copied! Go ahead and play around with it a little bit by speaking into your microphone and seeing how well SpeechRecognition transcribes your speech.

Note: If you are on Ubuntu and get some funky output like ‘ALSA lib … Unknown PCM’, refer to this page for tips on suppressing these messages. This output comes from the ALSA package installed with Ubuntu—not SpeechRecognition or PyAudio. In all reality, these messages may indicate a problem with your ALSA configuration, but in my experience, they do not impact the functionality of your code. They are mostly a nuisance.

Open up another interpreter session and create an instance of the recognizer class.

Now, instead of using an audio file as the source, you will use the default system microphone. You can access this by creating an instance of the Microphone class.

If your system has no default microphone (such as on a Raspberry Pi ), or you want to use a microphone other than the default, you will need to specify which one to use by supplying a device index. You can get a list of microphone names by calling the list_microphone_names() static method of the Microphone class.

Note that your output may differ from the above example.

The device index of the microphone is the index of its name in the list returned by list_microphone_names(). For example, given the above output, if you want to use the microphone called “front,” which has index 3 in the list, you would create a microphone instance like this:

For most projects, though, you’ll probably want to use the default system microphone.

Now that you’ve got a Microphone instance ready to go, it’s time to capture some input.

Just like the AudioFile class, Microphone is a context manager. You can capture input from the microphone using the listen() method of the Recognizer class inside of the with block. This method takes an audio source as its first argument and records input from the source until silence is detected.

Once you execute the with block, try speaking “hello” into your microphone. Wait a moment for the interpreter prompt to display again. Once the “>>>” prompt returns, you’re ready to recognize the speech.

If the prompt never returns, your microphone is most likely picking up too much ambient noise. You can interrupt the process with Ctrl + C to get your prompt back.

To handle ambient noise, you’ll need to use the adjust_for_ambient_noise() method of the Recognizer class, just like you did when trying to make sense of the noisy audio file. Since input from a microphone is far less predictable than input from an audio file, it is a good idea to do this anytime you listen for microphone input.

After running the above code, wait a second for adjust_for_ambient_noise() to do its thing, then try speaking “hello” into the microphone. Again, you will have to wait a moment for the interpreter prompt to return before trying to recognize the speech.

Recall that adjust_for_ambient_noise() analyzes the audio source for one second. If this seems too long to you, feel free to adjust this with the duration keyword argument.

The SpeechRecognition documentation recommends using a duration no less than 0.5 seconds. In some cases, you may find that durations longer than the default of one second generate better results. The minimum value you need depends on the microphone’s ambient environment. Unfortunately, this information is typically unknown during development. In my experience, the default duration of one second is adequate for most applications.

Try typing the previous code example in to the interpeter and making some unintelligible noises into the microphone. You should get something like this in response:

Audio that cannot be matched to text by the API raises an UnknownValueError exception. You should always wrap calls to the API with try and except blocks to handle this exception .

Note : You may have to try harder than you expect to get the exception thrown. The API works very hard to transcribe any vocal sounds. Even short grunts were transcribed as words like “how” for me. Coughing, hand claps, and tongue clicks would consistently raise the exception.

Now that you’ve seen the basics of recognizing speech with the SpeechRecognition package let’s put your newfound knowledge to use and write a small game that picks a random word from a list and gives the user three attempts to guess the word.

Here is the full script:

Let’s break that down a little bit.

The recognize_speech_from_mic() function takes a Recognizer and Microphone instance as arguments and returns a dictionary with three keys. The first key, "success" , is a boolean that indicates whether or not the API request was successful. The second key, "error" , is either None or an error message indicating that the API is unavailable or the speech was unintelligible. Finally, the "transcription" key contains the transcription of the audio recorded by the microphone.

The function first checks that the recognizer and microphone arguments are of the correct type, and raises a TypeError if either is invalid:

The listen() method is then used to record microphone input:

The adjust_for_ambient_noise() method is used to calibrate the recognizer for changing noise conditions each time the recognize_speech_from_mic() function is called.

Next, recognize_google() is called to transcribe any speech in the recording. A try...except block is used to catch the RequestError and UnknownValueError exceptions and handle them accordingly. The success of the API request, any error messages, and the transcribed speech are stored in the success , error and transcription keys of the response dictionary, which is returned by the recognize_speech_from_mic() function.

You can test the recognize_speech_from_mic() function by saving the above script to a file called “guessing_game.py” and running the following in an interpreter session:

The game itself is pretty simple. First, a list of words, a maximum number of allowed guesses and a prompt limit are declared:

Next, a Recognizer and Microphone instance is created and a random word is chosen from WORDS :

After printing some instructions and waiting for 3 three seconds, a for loop is used to manage each user attempt at guessing the chosen word. The first thing inside the for loop is another for loop that prompts the user at most PROMPT_LIMIT times for a guess, attempting to recognize the input each time with the recognize_speech_from_mic() function and storing the dictionary returned to the local variable guess .

If the "transcription" key of guess is not None , then the user’s speech was transcribed and the inner loop is terminated with break . If the speech was not transcribed and the "success" key is set to False , then an API error occurred and the loop is again terminated with break . Otherwise, the API request was successful but the speech was unrecognizable. The user is warned and the for loop repeats, giving the user another chance at the current attempt.

Once the inner for loop terminates, the guess dictionary is checked for errors. If any occurred, the error message is displayed and the outer for loop is terminated with break , which will end the program execution.

If there weren’t any errors, the transcription is compared to the randomly selected word. The lower() method for string objects is used to ensure better matching of the guess to the chosen word. The API may return speech matched to the word “apple” as “Apple” or “apple,” and either response should count as a correct answer.

If the guess was correct, the user wins and the game is terminated. If the user was incorrect and has any remaining attempts, the outer for loop repeats and a new guess is retrieved. Otherwise, the user loses the game.

When run, the output will look something like this:

In this tutorial, you’ve seen how to install the SpeechRecognition package and use its Recognizer class to easily recognize speech from both a file—using record() —and microphone input—using listen(). You also saw how to process segments of an audio file using the offset and duration keyword arguments of the record() method.

You’ve seen the effect noise can have on the accuracy of transcriptions, and have learned how to adjust a Recognizer instance’s sensitivity to ambient noise with adjust_for_ambient_noise(). You have also learned which exceptions a Recognizer instance may throw— RequestError for bad API requests and UnkownValueError for unintelligible speech—and how to handle these with try...except blocks.

Speech recognition is a deep subject, and what you have learned here barely scratches the surface. If you’re interested in learning more, here are some additional resources.

For more information on the SpeechRecognition package:

• Library reference
• Troubleshooting page

A few interesting internet resources:

• Behind the Mic: The Science of Talking with Computers . A short film about speech processing by Google.
• A Historical Perspective of Speech Recognition by Huang, Baker and Reddy. Communications of the ACM (2014). This article provides an in-depth and scholarly look at the evolution of speech recognition technology.
• The Past, Present and Future of Speech Recognition Technology by Clark Boyd at The Startup. This blog post presents an overview of speech recognition technology, with some thoughts about the future.

Some good books about speech recognition:

• The Voice in the Machine: Building Computers That Understand Speech , Pieraccini, MIT Press (2012). An accessible general-audience book covering the history of, as well as modern advances in, speech processing.
• Fundamentals of Speech Recognition , Rabiner and Juang, Prentice Hall (1993). Rabiner, a researcher at Bell Labs, was instrumental in designing some of the first commercially viable speech recognizers. This book is now over 20 years old, but a lot of the fundamentals remain the same.
• Automatic Speech Recognition: A Deep Learning Approach , Yu and Deng, Springer (2014). Yu and Deng are researchers at Microsoft and both very active in the field of speech processing. This book covers a lot of modern approaches and cutting-edge research but is not for the mathematically faint-of-heart.

Throughout this tutorial, we’ve been recognizing speech in English, which is the default language for each recognize_*() method of the SpeechRecognition package. However, it is absolutely possible to recognize speech in other languages, and is quite simple to accomplish.

To recognize speech in a different language, set the language keyword argument of the recognize_*() method to a string corresponding to the desired language. Most of the methods accept a BCP-47 language tag, such as 'en-US' for American English, or 'fr-FR' for French. For example, the following recognizes French speech in an audio file:

Only the following methods accept a language keyword argument:

• recognize_bing()
• recognize_google()
• recognize_google_cloud()
• recognize_ibm()
• recognize_sphinx()

To find out which language tags are supported by the API you are using, you’ll have to consult the corresponding documentation . A list of tags accepted by recognize_google() can be found in this Stack Overflow answer .

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Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data.  Research  (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

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Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

• Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

• Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
• N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
• Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.  While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
• Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

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Enable speech transcription in multiple languages for a variety of use cases, including but not limited to customer self-service, agent assistance and speech analytics.

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Essential Guide to Automatic Speech Recognition Technology

Over the past decade, AI-powered speech recognition systems have slowly become part of our everyday lives, from voice search to virtual assistants in contact centers, cars, hospitals, and restaurants. These speech recognition developments are made possible by deep learning advancements.

Developers across many industries now use automatic speech recognition (ASR) to increase business productivity, application efficiency, and even digital accessibility. This post discusses ASR, how it works, use cases, advancements, and more.

What is automatic speech recognition?

Speech recognition technology is capable of converting spoken language (an audio signal) into written text that is often used as a command.

Today’s most advanced software can accurately process varying language dialects and accents. For example, ASR is commonly seen in user-facing applications such as virtual agents, live captioning, and clinical note-taking. Accurate speech transcription is essential for these use cases.

Developers in the speech AI space also use  alternative terminologies  to describe speech recognition such as ASR, speech-to-text (STT), and voice recognition.

ASR is a critical component of  speech AI , which is a suite of technologies designed to help humans converse with computers through voice.

Why natural language processing is used in speech recognition

Developers are often unclear about the role of natural language processing (NLP) models in the ASR pipeline. Aside from being applied in language models, NLP is also used to augment generated transcripts with punctuation and capitalization at the end of the ASR pipeline.

After the transcript is post-processed with NLP, the text is used for downstream language modeling tasks:

• Sentiment analysis
• Text analytics
• Text summarization
• Question answering

Speech recognition algorithms

Speech recognition algorithms can be implemented in a traditional way using statistical algorithms or by using deep learning techniques such as neural networks to convert speech into text.

Traditional ASR algorithms

Hidden Markov models (HMM) and dynamic time warping (DTW) are two such examples of traditional statistical techniques for performing speech recognition.

Using a set of transcribed audio samples, an HMM is trained to predict word sequences by varying the model parameters to maximize the likelihood of the observed audio sequence.

DTW is a dynamic programming algorithm that finds the best possible word sequence by calculating the distance between time series: one representing the unknown speech and others representing the known words.

Deep learning ASR algorithms

For the last few years, developers have been interested in deep learning for speech recognition because statistical algorithms are less accurate. In fact, deep learning algorithms work better at understanding dialects, accents, context, and multiple languages, and they transcribe accurately even in noisy environments.

Some of the most popular state-of-the-art speech recognition acoustic models are Quartznet , Citrinet , and Conformer . In a typical speech recognition pipeline, you can choose and switch any acoustic model that you want based on your use case and performance.

Implementation tools for deep learning models

Several tools are available for developing deep learning speech recognition models and pipelines, including Kaldi , Mozilla DeepSpeech, NVIDIA NeMo , NVIDIA Riva , NVIDIA TAO Toolkit , and services from Google, Amazon, and Microsoft.

Kaldi, DeepSpeech, and NeMo are open-source toolkits that help you build speech recognition models. TAO Toolkit and Riva are closed-source SDKs that help you develop customizable pipelines that can be deployed in production.

Cloud service providers like Google, AWS, and Microsoft offer generic services that you can easily plug and play with.

Deep learning speech recognition pipeline

An ASR pipeline consists of the following components:

• Spectrogram generator that converts raw audio to spectrograms.
• Acoustic model that takes the spectrograms as input and outputs a matrix of probabilities over characters over time.
• Decoder (optionally coupled with a language model) that generates possible sentences from the probability matrix.
• Punctuation and capitalization model that formats the generated text for easier human consumption.

A typical deep learning pipeline for speech recognition includes the following components:

• Data preprocessing
• Neural acoustic model
• Decoder (optionally coupled with an n-gram language model)
• Punctuation and capitalization model

Figure 1 shows an example of a deep learning speech recognition pipeline:.

Datasets are essential in any deep learning application. Neural networks function similarly to the human brain. The more data you use to teach the model, the more it learns. The same is true for the speech recognition pipeline.

A few popular speech recognition datasets are

• LibriSpeech
• Fisher English Training Speech
• Mozilla Common Voice (MCV)
• 2000 HUB 5 English Evaluation Speech
• AN4 (includes recordings of people spelling out addresses and names)
• Aishell-1/AIshell-2 Mandarin speech corpus

Data processing is the first step. It includes data preprocessing and augmentation techniques such as speed/time/noise/impulse perturbation and time stretch augmentation, fast Fourier Transformations (FFT) using windowing, and normalization techniques.

For example, in Figure 2, the mel spectrogram is generated from a raw audio waveform after applying FFT using the windowing technique.

We can also use perturbation techniques to augment the training dataset. Figures 3 and 4 represent techniques like noise perturbation and masking being used to increase the size of the training dataset in order to avoid problems like overfitting.

The output of the data preprocessing stage is a spectrogram/mel spectrogram, which is a visual representation of the strength of the audio signal over time. 

Mel spectrograms are then fed into the next stage: a neural acoustic model . QuartzNet, CitriNet, ContextNet, Conformer-CTC, and Conformer-Transducer are examples of cutting-edge neural acoustic models. Multiple ASR models exist for several reasons, such as the need for real-time performance, higher accuracy, memory size, and compute cost for your use case.

However, Conformer-based models are becoming more popular due to their improved accuracy and ability to comprehend. The acoustic model returns the probability of characters/words at each time stamp.

Figure 5 shows the output of the acoustic model, with time stamps. 

The acoustic model’s output is fed into the decoder along with the language model. Decoders include beam search and greedy decoders, and language models include n-gram language, KenLM, and neural scoring. When it comes to the decoder, it helps to generate top words, which are then passed to language models to predict the correct sentence.

In Figure 6, the decoder selects the next best word based on the probability score. Based on the final highest score, the correct word or sentence is selected and sent to the punctuation and capitalization model.

The ASR pipeline generates text with no punctuation or capitalization.

Finally, a punctuation and capitalization model is used to improve the text quality for better readability. Bidirectional Encoder Representations from Transformers (BERT) models are commonly used to generate punctuated text.

Figure 7 shows a simple example of a before-and-after punctuation and capitalization model.

Speech recognition industry impact

There are many unique applications for ASR . For example, speech recognition could help industries such as finance, telecommunications, and unified communications as a service (UCaaS) to improve customer experience, operational efficiency, and return on investment (ROI).

Speech recognition is applied in the finance industry for applications such as call center agent assist and trade floor transcripts. ASR is used to transcribe conversations between customers and call center agents or trade floor agents. The generated transcriptions can then be analyzed and used to provide real-time recommendations to agents. This adds to an 80% reduction in post-call time.

Furthermore, the generated transcripts are used for downstream tasks:

• Intent and entity recognition

Telecommunications

Contact centers are critical components of the telecommunications industry. With contact center technology, you can reimagine the telecommunications customer center, and speech recognition helps with that.

As previously discussed in the finance call center use case, ASR is used in Telecom contact centers to transcribe conversations between customers and contact center agents to analyze them and recommend call center agents in real time. T-Mobile uses ASR for quick customer resolution , for example.

Unified communications as a software

COVID-19 increased demand for UCaaS solutions, and vendors in the space began focusing on the use of speech AI technologies such as ASR to create more engaging meeting experiences.

For example, ASR can be used to generate live captions in video conferencing meetings. Captions generated can then be used for downstream tasks such as meeting summaries and identifying action items in notes.

Future of ASR technology

Speech recognition is not as easy as it sounds. Developing speech recognition is full of challenges, ranging from accuracy to customization for your use case to real-time performance. On the other hand, businesses and academic institutions are racing to overcome some of these challenges and advance the use of speech recognition capabilities.

ASR challenges

Some of the challenges in developing and deploying speech recognition pipelines in production include the following:

• Lack of tools and SDKs that offer state-of-the-art (SOTA) ASR models makes it difficult for developers to take advantage of the best speech recognition technology.
• Limited customization capabilities that enable developers to fine-tune on domain-specific and context-specific jargon, multiple languages, dialects, and accents to have your applications understand and speak like you
• Restricted deployment support; for example, depending on the use case, the software should be capable of being deployed in any cloud, on-premises, edge, and embedded. 
• Real-time speech recognition pipelines; for instance, in a call center agent assist use case, we cannot wait several seconds for conversations to be transcribed before using them to empower agents.

For more information about the major pain points that developers face when adding speech-to-text capabilities to applications, see Solving Automatic Speech Recognition Deployment Challenges .

ASR advancements

Numerous advancements in speech recognition are occurring on both the research and software development fronts. To begin, research has resulted in the development of several new cutting-edge ASR architectures, E2E speech recognition models, and self-supervised or unsupervised training techniques.

On the software side, there are a few tools that enable quick access to SOTA models, and then there are different sets of tools that enable the deployment of models as services in production. 

Key takeaways

Speech recognition continues to grow in adoption due to its advancements in deep learning-based algorithms that have made ASR as accurate as human recognition. Also, breakthroughs like multilingual ASR help companies make their apps available worldwide, and moving algorithms from cloud to on-device saves money, protects privacy, and speeds up inference.

NVIDIA offers Riva , a speech AI SDK, to address several of the challenges discussed above. With Riva, you can quickly access the latest SOTA research models tailored for production purposes. You can customize these models to your domain and use case, deploy on any cloud, on-premises, edge, or embedded, and run them in real-time for engaging natural interactions.

Learn how your organization can benefit from speech recognition skills with the free ebook, Building Speech AI Applications .

Related resources

• GTC session: Speech AI Demystified
• GTC session: Mastering Speech AI for Multilingual Multimedia Transformation
• GTC session: Human-Like AI Voices: Exploring the Evolution of Voice Technology
• NGC Containers: Domain Specific NeMo ASR Application
• NGC Containers: MATLAB
• Webinar: How Telcos Transform Customer Experiences with Conversational AI

About the Authors

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Simple audio recognition: Recognizing keywords

This tutorial demonstrates how to preprocess audio files in the WAV format and build and train a basic automatic speech recognition (ASR) model for recognizing ten different words. You will use a portion of the Speech Commands dataset ( Warden, 2018 ), which contains short (one-second or less) audio clips of commands, such as "down", "go", "left", "no", "right", "stop", "up" and "yes".

Real-world speech and audio recognition systems are complex. But, like image classification with the MNIST dataset , this tutorial should give you a basic understanding of the techniques involved.

Import necessary modules and dependencies. You'll be using tf.keras.utils.audio_dataset_from_directory (introduced in TensorFlow 2.10), which helps generate audio classification datasets from directories of .wav files. You'll also need seaborn for visualization in this tutorial.

Import the mini Speech Commands dataset

To save time with data loading, you will be working with a smaller version of the Speech Commands dataset. The original dataset consists of over 105,000 audio files in the WAV (Waveform) audio file format of people saying 35 different words. This data was collected by Google and released under a CC BY license.

Download and extract the mini_speech_commands.zip file containing the smaller Speech Commands datasets with tf.keras.utils.get_file :

The dataset's audio clips are stored in eight folders corresponding to each speech command: no , yes , down , go , left , up , right , and stop :

Divided into directories this way, you can easily load the data using keras.utils.audio_dataset_from_directory .

The audio clips are 1 second or less at 16kHz. The output_sequence_length=16000 pads the short ones to exactly 1 second (and would trim longer ones) so that they can be easily batched.

The dataset now contains batches of audio clips and integer labels. The audio clips have a shape of (batch, samples, channels) .

This dataset only contains single channel audio, so use the tf.squeeze function to drop the extra axis:

The utils.audio_dataset_from_directory function only returns up to two splits. It's a good idea to keep a test set separate from your validation set. Ideally you'd keep it in a separate directory, but in this case you can use Dataset.shard to split the validation set into two halves. Note that iterating over any shard will load all the data, and only keep its fraction.

Let's plot a few audio waveforms:

Convert waveforms to spectrograms

The waveforms in the dataset are represented in the time domain. Next, you'll transform the waveforms from the time-domain signals into the time-frequency-domain signals by computing the short-time Fourier transform (STFT) to convert the waveforms to as spectrograms , which show frequency changes over time and can be represented as 2D images. You will feed the spectrogram images into your neural network to train the model.

A Fourier transform ( tf.signal.fft ) converts a signal to its component frequencies, but loses all time information. In comparison, STFT ( tf.signal.stft ) splits the signal into windows of time and runs a Fourier transform on each window, preserving some time information, and returning a 2D tensor that you can run standard convolutions on.

Create a utility function for converting waveforms to spectrograms:

• The waveforms need to be of the same length, so that when you convert them to spectrograms, the results have similar dimensions. This can be done by simply zero-padding the audio clips that are shorter than one second (using tf.zeros ).
• When calling tf.signal.stft , choose the frame_length and frame_step parameters such that the generated spectrogram "image" is almost square. For more information on the STFT parameters choice, refer to this Coursera video on audio signal processing and STFT.
• The STFT produces an array of complex numbers representing magnitude and phase. However, in this tutorial you'll only use the magnitude, which you can derive by applying tf.abs on the output of tf.signal.stft .

Next, start exploring the data. Print the shapes of one example's tensorized waveform and the corresponding spectrogram, and play the original audio:

Your browser does not support the audio element.

Now, define a function for displaying a spectrogram:

Plot the example's waveform over time and the corresponding spectrogram (frequencies over time):

Now, create spectrogram datasets from the audio datasets:

Examine the spectrograms for different examples of the dataset:

Build and train the model

Add Dataset.cache and Dataset.prefetch operations to reduce read latency while training the model:

For the model, you'll use a simple convolutional neural network (CNN), since you have transformed the audio files into spectrogram images.

Your tf.keras.Sequential model will use the following Keras preprocessing layers:

• tf.keras.layers.Resizing : to downsample the input to enable the model to train faster.
• tf.keras.layers.Normalization : to normalize each pixel in the image based on its mean and standard deviation.

For the Normalization layer, its adapt method would first need to be called on the training data in order to compute aggregate statistics (that is, the mean and the standard deviation).

Configure the Keras model with the Adam optimizer and the cross-entropy loss:

Train the model over 10 epochs for demonstration purposes:

Let's plot the training and validation loss curves to check how your model has improved during training:

Evaluate the model performance

Run the model on the test set and check the model's performance:

Display a confusion matrix

Use a confusion matrix to check how well the model did classifying each of the commands in the test set:

Run inference on an audio file

Finally, verify the model's prediction output using an input audio file of someone saying "no". How well does your model perform?

As the output suggests, your model should have recognized the audio command as "no".

Export the model with preprocessing

The model's not very easy to use if you have to apply those preprocessing steps before passing data to the model for inference. So build an end-to-end version:

Test run the "export" model:

Save and reload the model, the reloaded model gives identical output:

This tutorial demonstrated how to carry out simple audio classification/automatic speech recognition using a convolutional neural network with TensorFlow and Python. To learn more, consider the following resources:

• The Sound classification with YAMNet tutorial shows how to use transfer learning for audio classification.
• The notebooks from Kaggle's TensorFlow speech recognition challenge .
• The TensorFlow.js - Audio recognition using transfer learning codelab teaches how to build your own interactive web app for audio classification.
• A tutorial on deep learning for music information retrieval (Choi et al., 2017) on arXiv.
• TensorFlow also has additional support for audio data preparation and augmentation to help with your own audio-based projects.
• Consider using the librosa library for music and audio analysis.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-03-23 UTC.

• Customer service and contact center

speech recognition

• Ben Lutkevich, Site Editor
• Karolina Kiwak

What is speech recognition?

Speech recognition, or speech-to-text, is the ability of a machine or program to identify words spoken aloud and convert them into readable text. Rudimentary speech recognition software has a limited vocabulary and may only identify words and phrases when spoken clearly. More sophisticated software can handle natural speech, different accents and various languages.

Speech recognition uses a broad array of research in computer science, linguistics and computer engineering. Many modern devices and text-focused programs have speech recognition functions in them to allow for easier or hands-free use of a device.

Speech recognition and voice recognition are two different technologies and should not be confused :

• Speech recognition is used to identify words in spoken language.
• Voice recognition is a biometric technology for identifying an individual's voice.

How does speech recognition work?

Speech recognition systems use computer algorithms to process and interpret spoken words and convert them into text. A software program turns the sound a microphone records into written language that computers and humans can understand, following these four steps:

• analyze the audio;
• break it into parts;
• digitize it into a computer-readable format; and
• use an algorithm to match it to the most suitable text representation.

Speech recognition software must adapt to the highly variable and context-specific nature of human speech. The software algorithms that process and organize audio into text are trained on different speech patterns, speaking styles, languages, dialects, accents and phrasings. The software also separates spoken audio from background noise that often accompanies the signal.

To meet these requirements, speech recognition systems use two types of models:

• Acoustic models. These represent the relationship between linguistic units of speech and audio signals.
• Language models. Here, sounds are matched with word sequences to distinguish between words that sound similar.

What applications is speech recognition used for?

Speech recognition systems have quite a few applications. Here is a sampling of them.

Mobile devices. Smartphones use voice commands for call routing, speech-to-text processing, voice dialing and voice search. Users can respond to a text without looking at their devices. On Apple iPhones, speech recognition powers the keyboard and Siri, the virtual assistant. Functionality is available in secondary languages, too. Speech recognition can also be found in word processing applications like Microsoft Word, where users can dictate words to be turned into text.

Education. Speech recognition software is used in language instruction. The software hears the user's speech and offers help with pronunciation.

Customer service. Automated voice assistants listen to customer queries and provides helpful resources.

Healthcare applications. Doctors can use speech recognition software to transcribe notes in real time into healthcare records.

Disability assistance. Speech recognition software can translate spoken words into text using closed captions to enable a person with hearing loss to understand what others are saying. Speech recognition can also enable those with limited use of their hands to work with computers, using voice commands instead of typing.

Court reporting. Software can be used to transcribe courtroom proceedings, precluding the need for human transcribers.

Emotion recognition. This technology can analyze certain vocal characteristics to determine what emotion the speaker is feeling. Paired with sentiment analysis, this can reveal how someone feels about a product or service.

Hands-free communication. Drivers use voice control for hands-free communication, controlling phones, radios and global positioning systems, for instance.

What are the features of speech recognition systems?

Good speech recognition programs let users customize them to their needs. The features that enable this include:

• Language weighting. This feature tells the algorithm to give special attention to certain words, such as those spoken frequently or that are unique to the conversation or subject. For example, the software can be trained to listen for specific product references.
• Acoustic training. The software tunes out ambient noise that pollutes spoken audio. Software programs with acoustic training can distinguish speaking style, pace and volume amid the din of many people speaking in an office.
• Speaker labeling. This capability enables a program to label individual participants and identify their specific contributions to a conversation.
• Profanity filtering. Here, the software filters out undesirable words and language.

What are the different speech recognition algorithms?

The power behind speech recognition features comes from a set of algorithms and technologies. They include the following:

• Hidden Markov model. HMMs are used in autonomous systems where a state is partially observable or when all of the information necessary to make a decision is not immediately available to the sensor (in speech recognition's case, a microphone). An example of this is in acoustic modeling, where a program must match linguistic units to audio signals using statistical probability.
• Natural language processing. NLP eases and accelerates the speech recognition process.
• N-grams. This simple approach to language models creates a probability distribution for a sequence. An example would be an algorithm that looks at the last few words spoken, approximates the history of the sample of speech and uses that to determine the probability of the next word or phrase that will be spoken.
• Artificial intelligence. AI and machine learning methods like deep learning and neural networks are common in advanced speech recognition software. These systems use grammar, structure, syntax and composition of audio and voice signals to process speech. Machine learning systems gain knowledge with each use, making them well suited for nuances like accents.

What are the advantages of speech recognition?

There are several advantages to using speech recognition software, including the following:

• Machine-to-human communication. The technology enables electronic devices to communicate with humans in natural language or conversational speech.
• Readily accessible. This software is frequently installed in computers and mobile devices, making it accessible.
• Easy to use. Well-designed software is straightforward to operate and often runs in the background.
• Continuous, automatic improvement. Speech recognition systems that incorporate AI become more effective and easier to use over time. As systems complete speech recognition tasks, they generate more data about human speech and get better at what they do.

What are the disadvantages of speech recognition?

While convenient, speech recognition technology still has a few issues to work through. Limitations include:

• Inconsistent performance. The systems may be unable to capture words accurately because of variations in pronunciation, lack of support for some languages and inability to sort through background noise. Ambient noise can be especially challenging. Acoustic training can help filter it out, but these programs aren't perfect. Sometimes it's impossible to isolate the human voice.
• Speed. Some speech recognition programs take time to deploy and master. The speech processing may feel relatively slow .
• Source file issues. Speech recognition success depends on the recording equipment used, not just the software.

The takeaway

Speech recognition is an evolving technology. It is one of the many ways people can communicate with computers with little or no typing. A variety of communications-based business applications capitalize on the convenience and speed of spoken communication that this technology enables.

Speech recognition programs have advanced greatly over 60 years of development. They are still improving, fueled in particular by AI.

Learn more about the AI-powered business transcription software in this Q&A with Wilfried Schaffner, chief technology officer of Speech Processing Solutions.

Continue Reading About speech recognition

• How can speech recognition technology support remote work?
• Automatic speech recognition may be better than you think
• Speech recognition use cases enable touchless collaboration
• Automated speech recognition gives CX vendor an edge
• Speech API from Mozilla's Web developer platform

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Automatic Speech Recognition: Types and Examples

• Yashoda Gandhi
• Mar 02, 2022

Voice assistants such as Google Home, Amazon Echo, Siri, Cortana, and others have become increasingly popular in recent years. These are some of the most well-known examples of automatic speech recognition (ASR). 

This type of app starts with a clip of spoken audio in a specific language and converts the words spoken into text. As a result, they're also called Speech-to-Text algorithms.

Apps like Siri and the others mentioned above, of course, go even further. They not only extract the text but also interpret and comprehend the semantic meaning of what was said, allowing them to respond to the user's commands with answers or actions.

Automatic Speech Recognition

ASR (Automated speech recognition) is a technology that allows users to enter data into information systems by speaking rather than punching numbers into a keypad. ASR is primarily used for providing information and forwarding phone calls.

In recent years, ASR has grown in popularity among large corporation customer service departments. It is also used by some government agencies and other organizations. Basic ASR systems recognize single-word entries such as yes-or-no responses and spoken numerals. 

This enables users to navigate through automated menus without having to manually enter dozens of numerals with no margin for error. In a manual-entry situation, a customer may press the wrong key after entering  20 or 30 numerals at random intervals in the menu and abandon the call rather than call back and start over. This issue is virtually eliminated with ASR.

Natural Language Processing, or NLP for short, is at the heart of the most advanced version of currently available ASR technologies. Though this variant of ASR is still a long way from realizing its full potential, we're already seeing some impressive results in the form of intelligent smartphone interfaces like Apple's Siri and other systems used in business and advanced technology.

Even with a "accuracy" of 96 to 99 percent , these NLP programs can only achieve these kinds of results under ideal circumstances, such as when humans ask them simple yes or no questions with a small number of possible responses based on selected keywords.

Also Read |  A Step Towards Artificial Super Intelligence (ASI)

How to carry out Automatic Speech Recognition ?

We’ve listed three significant ways for automatic speech recognition. 

Old fashioned way

With ARPA funding in the 1970s, a team at Carnegie Melon University developed technology that could generate transcripts from context-specific speech, such as voice-controlled chess, chart-plotting for GIS and navigation, and document management in the office environment.

These types of products had one major flaw: they could only reliably convert speech to text for one person at a time. This is due to the fact that no two people speak in the same way. In fact, even if the same person speaks the same sentence twice, the sounds are mathematically different when recorded and measured!

Two mathematical realities for silicon brains, the same word to our human, meat-based brains! These ASR-based, personal transcription tools and products were revolutionary and had legitimate business uses, despite their inability to transcribe the utterances of multiple speakers.

Frankenstein approach

In the mid-2000s, companies like Nuance, Google, and Amazon realized that by making ASR work for multiple speakers and in noisy environments, they could improve on the 1970s approach.

Rather than having to train ASR to understand a single speaker, these Franken-ASRs were able to understand multiple speakers fairly well, which is an impressive feat given the acoustic and mathematical realities of spoken language. This is possible because these neural-network algorithms can "learn on their own" when given certain stimuli.

However, slapping a neural network on top of older machinery (remember, this is based on 1970s techniques) results in bulky, complex, and resource-hungry machines like Back-to-the-DeLorean Future's or my college bicycle: a franken-bike that worked when the tides and winds were just right, usually except when it didn't.

While clumsy, the mid-2000s hybrid approach to ASR works well enough for some applications; after all, Siri isn't supposed to answer any real-world data questions.

End to end Deep Learning

The most recent method, end-to-end deep learning ASR, makes use of neural networks and replaces the clumsy 1970s method. In essence, this new approach allows you to do something that was unthinkable even two years ago: train the ASR to recognize dialects, accents, and industry-specific word sets quickly and accurately.

It's a Mr. Fusion bicycle, complete with rusted bike frames and ill-fated auto brands. Several factors contribute to this, including breakthrough math from the 1980s, computing power/technology from the mid-2010s, big data, and the ability to innovate quickly.

It's crucial to be able to experiment with new architectures, technologies, and approaches. Legacy ASR systems based on the franken-ASR hybrid are designed to handle "general" audio rather than specialized audio for industry, business, or even academic purposes.To put it another way, they provide generalized speech recognition and cannot realistically be trained to improve your speech data.

Also Read |  Speech Analytics

Types of ASR

The  two main types of Automatic Speech Recognition software  variants are directed dialogue conversations and natural language conversations. 

Detecting a direct dialogue speech

Directed Dialogue conversations are a much less complicated version of ASR at work, consisting of machine interfaces that instruct you to respond verbally with a specific word from a limited list of options, forming their response to your narrowly defined request. Directed conversation Automated telephone banking and other customer service interfaces frequently use ASR software.

Analyze natural language conversation

Natural Language Conversations (the NLP we discussed in the introduction) are more advanced versions of ASR that attempt to simulate real conversation by allowing you to use an open-ended chat format with them rather than a severely limited menu of words. One of the most advanced examples of these systems is the Siri interface on the iPhone.

Applications of ASR

Where continuous conversations must be tracked or recorded word for word, ASR is used in a variety of industries, including higher education, legal, finance, government, health care, and the media.

In legal proceedings, it's critical to record every word, and court reporters are in short supply right now. ASR technology has several advantages, including digital transcription and scalability.

ASR can be used by universities to provide captions and transcriptions in the classroom for students with hearing loss or other disabilities. It can also benefit non-native English speakers, commuters, and students with a variety of learning needs.

ASR is used by doctors to transcribe notes from patient meetings or to document surgical procedures.

Media companies can use ASR to provide live captions and media transcription for all of their productions.

Businesses use ASR for captioning and transcription to make training materials more accessible and to create more inclusive workplaces.

Also Read |  Hyper Automation

Advantages of ASR over Traditional Transcriptions

We’ve listed some advantages of ASR over Traditional Transcriptions below :

ASR machines can help improve caption and transcription efficiencies, in addition to the growing shortage of skilled traditional transcribers.

In conversations, lectures, meetings, and proceedings, the technology can distinguish between voices, allowing you to figure out who said what and when.

Because disruptions among participants are common in these conversations with multiple stakeholders, the ability to distinguish between speakers can be very useful.

Users can train the ASR machine by uploading hundreds of related documents, such as books, articles, and other materials.

The technology can absorb this vast amount of data faster than a human, allowing it to recognize different accents, dialects, and terminology with greater accuracy.

Of course, in order to achieve the near-perfect accuracy required, the ideal format would involve using human intelligence to fact-check the artificial intelligence that is being used.

Automatic Speech Recognition Systems (ASRs) can convert spoken words into understandable text.

Its application to air traffic control and automated car environments has been studied due to its ability to convert speech in real-time.

The Hidden Markov model is used in feature extraction by the ASR system for air traffic control, and its phraseology is based on the commands used in air applications.

Speech recognition is used in the car environment for route navigation applications.

Also Read |  Artificial Intelligence vs Human Intelligence

Automatic Speech Recognition vs Voice Recognition

The difference between Voice Recognition and Automatic Speech Recognition (the technical term for AI speech recognition, or ASR) is how they process and respond to audio.

 You'll be able to use voice recognition with devices like Amazon Alexa or Google Dot. It listens to your voice and responds in real-time. Most digital assistants use voice recognition, which has limited functionality and is usually limited to the task at hand.

ASR differs from other voice recognition systems in that it recognizes speech rather than voices. It can accurately generate an audio transcript using NLP, resulting in real-time captioning. ASR isn't perfect; in fact, even under ideal conditions, it rarely exceeds  90%-95 percent accuracy . However, it compensates for this by being quick and inexpensive.

In essence, ASR is a transcription of what someone said, whereas Voice Recognition is a transcription of who said it. Both processes are inextricably linked, and they are frequently used interchangeably. The distinctions are subtle but noticeable.

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7 real-world examples of voice recognition technology.

Speech recognition technology is the hub of millions of homes worldwide – devices that listen to your voice and carry out a subsequent command. You may think that technology doesn’t extend much further, but you might want to grab a ladder – this hole is a deep one.

The technology within speech recognition software goes beyond what most of us know. Speech-to-text, such as Speechmatics’ Autonomous Speech Recognition (ASR), stretches its influence across society. This article will dive into seven examples of speech recognition and areas where speech-to-text technology makes a valuable difference.

1) Doctor’s Virtual Assistant

Despite having vastly different healthcare systems, both the US and the UK suffer from extended wait times . It’s clear that hospitals around the world would benefit from anything that saves them time.

If doctors have easy access to speech-to-text technology, they shorten the average appointment by converting their notes from speech to text instead of transcribing by hand. The less time a doctor spends typing their notes, the more patients they can see during a day.

Furthermore, effective speech recognition systems such as our world-leading ASR cuts out the middleman more frequently. Instead of waiting for a human operative, many medical institutions use speech recognition to help you identify your symptoms and whether you need a doctor.

There is, however, a concern with the information speech-to-text software would ingest – it would likely need to be validated by recognized medical institutions from a data security perspective.

Despite this, speech-to-text in healthcare seems like a no brainer. When you save time, you save lives.

2) Autonomous Bank Deposits

According to a survey from PwC, 32% of customers will ditch a brand they love after a singular negative experience. Good customer service is vital to keeping customers and enticing new ones.

Banks often struggle with customer service, as customers get bounced from employee to manager, explaining the same details repeatedly. This is where speech-to-text software comes into play. As we move further into the 2020s, banks are adapting their services to the technology available.

There are numerous instances of major banks using speech-to-text technology. The Royal Bank of Canada, for example, lets customers pay bills using voice commands. The USAA offers members access to information about account balances, transactions, and spending patterns through Amazon’s Alexa. Banks such as U.S. Bank Smart Assistant provide tips and insights to help customers with their money. If banks want to reduce the need for human employees where possible.

3) Personalizing Adverts

“My phone keeps listening to me!” seems to pop up in modern conversation more and more these days.

What may seem like spyware is in fact speech-to-text technology collecting your data. Your devices listen for accents, speech patterns, and specific vocabulary used to find a consumer’s age, location, and other information. The software then collates that data into keywords which are then fed to you in the form of personalized ads.

While tracking your search history is vital for marketers, speech-to-text offers a more thorough behavior assessment. Text is often quite limited – you say what you need to say in as little words as possible. Speaking is more fluid and offers a better glimpse into your behavior, so by capturing that, marketers can tailor ads more to your needs.

4) Making Our Home Lives Easier

According to Statista, over 5 billion people will use voice-activated search in 2021, with predicted numbers reaching 6.4 billion in 2022. In addition, 30% of voice-assistant customers say they bought the software to control their homes.

In essence, people use speech recognition technology to make their lives easier. It's 2022, why should we trek over to the light switch to turn it on?

The pandemic pushed speech-to-text technology to greater heights, as people ordered shopping through Alexa, Siri, and co more often. Life is becoming as automated as possible.

5) Handsfree Playlist Shuffling

Take a seat in most modern cars and you’ll see ‘Apple CarPlay’ appear on the center console. This allows you to answer and make phone calls, change songs, send messages, and get directions without taking your hands off the steering wheel.

Not only do these features dramatically increase road safety, but they also make the driving experience more comfortable. You don’t need to queue fifty songs in a row and print off directions to your destination. Instead, speech recognition hears your request to send a text message, transcribes, and sends.

None of that would be possible without technology like speech-to-text.

6) Productivity Manager

COVID-19 changed the workplace forever. Offices have adapted since 2020, with many adopting a hybrid approach to working. Speechmatics is no different. Many of our employees work remotely, some work in our head office, and others started using our newly rented WeWork office spaces.

Organizations need to stay modern, or risk being left behind. Speech-to-text technology helps maintain productivity and efficiency no matter where employees are based. Microsoft Teams and Zoom are now office essentials. Emails and documents are transcribed without typing, saving time and hassle.

Meeting minutes are recorded and transcribed so absent workers can catch up. All of this allows for a more forgiving environment where employees can claim back some agency.

7) Giving Air Force Pilots Less to Think About

Fighter planes are the technological pinnacle of most nations’ weapons arsenal. The RAF’s EuroFighter Typhoon, for example, is one of the most feared jets on the planet. A large part of its operating system is done using speech recognition software . The pilot creates a template used for an array of cockpit functions, lightening their workload.

Step back onto the ground and speech-to-text technology is still just as prevalent. Speech recognition helps soldiers access vital mission information, consult maps, and transmit messages in the heat of battle.

Step back even further into government and speech recognition is everywhere. Departments often use it in place of a human operative, saving labor and money.

Speech Recognition Is Everywhere

In this day and age, you’ll be hard-pressed to find an area of your life not influenced by speech recognition technology. The scale is colossal, as while you tell Apple CarPlay to reply to your partner’s message, a doctor is shifting through their transcribed notes, and a fighter pilot is telling their plane to lock onto a target.

Of course, there are still many challenges – the technology is far from perfect – but the benefits are there for all to see. We at Speechmatics will continue to ensure the world reaps ASR’s potential rewards.

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Speech recognition or speech-to-text recognition, is the capacity of a machine or program to recognize spoken words and transform them into text. Speech Recognition  is an important feature in several applications used such as home automation, artificial intelligence, etc. In this article, we are going to discuss every point about What is Speech Recognition.

What is speech recognition in a Computer?

Speech Recognition , also known as automatic speech recognition ( ASR ), computer speech recognition, or speech-to-text, focuses on enabling computers to understand and interpret human speech. Speech recognition involves converting spoken language into text or executing commands based on the recognized words. This technology relies on sophisticated algorithms and machine learning models to process and understand human speech in real-time , despite the variations in accents , pitch , speed , and slang .

Key Features of Speech Recognition

• Accuracy and Speed: They can process speech in real-time or near real-time, providing quick responses to user inputs.
• Natural Language Understanding (NLU): NLU enables systems to handle complex commands and queries , making technology more intuitive and user-friendly .
• Multi-Language Support: Support for multiple languages and dialects , allowing users from different linguistic backgrounds to interact with technology in their native language.
• Background Noise Handling: This feature is crucial for voice-activated systems used in public or outdoor settings.

Speech Recognition Algorithms

Speech recognition technology relies on complex algorithms to translate spoken language into text or commands that computers can understand and act upon. Here are the algorithms and approaches used in speech recognition:

1. Hidden Markov Models (HMM)

Hidden Markov Models have been the backbone of speech recognition for many years. They model speech as a sequence of states, with each state representing a phoneme (basic unit of sound) or group of phonemes. HMMs are used to estimate the probability of a given sequence of sounds, making it possible to determine the most likely words spoken. Usage : Although newer methods have surpassed HMM in performance, it remains a fundamental concept in speech recognition, often used in combination with other techniques.

2. Natural language processing (NLP)

NLP is the area of  artificial intelligence  which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search. Example such as : Siri or provide more accessibility around texting. 

3. Deep Neural Networks (DNN)

DNNs have improved speech recognition’s accuracy a lot. These networks can learn hierarchical representations of data, making them particularly effective at modeling complex patterns like those found in human speech. DNNs are used both for acoustic modeling , to better understand the sound of speech , and for language modeling, to predict the likelihood of certain word sequences.

4. End-to-End Deep Learning

Now, the trend has shifted towards end-to-end deep learning models , which can directly map speech inputs to text outputs without the need for intermediate phonetic representations. These models, often based on advanced RNNs , Transformers, or Attention Mechanisms , can learn more complex patterns and dependencies in the speech signal.

What is Automatic Speech Recognition?

Automatic Speech Recognition (ASR) is a technology that enables computers to understand and transcribe spoken language into text. It works by analyzing audio input, such as spoken words, and converting them into written text , typically in real-time. ASR systems use algorithms and machine learning techniques to recognize and interpret speech patterns , phonemes, and language models to accurately transcribe spoken words. This technology is widely used in various applications, including virtual assistants , voice-controlled devices , dictation software , customer service automation , and language translation services .

What is Dragon speech recognition software?

Dragon speech recognition software is a program developed by Nuance Communications that allows users to dictate text and control their computer using voice commands. It transcribes spoken words into written text in real-time , enabling hands-free operation of computers and devices. Dragon software is widely used for various purposes, including dictating documents , composing emails , navigating the web , and controlling applications . It also features advanced capabilities such as voice commands for editing and formatting text , as well as custom vocabulary and voice profiles for improved accuracy and personalization.

What is a normal speech recognition threshold?

The normal speech recognition threshold refers to the level of sound, typically measured in decibels (dB) , at which a person can accurately recognize speech. In quiet environments, this threshold is typically around 0 to 10 dB for individuals with normal hearing. However, in noisy environments or for individuals with hearing impairments , the threshold may be higher, meaning they require a louder volume to accurately recognize speech .

Speech Recognition Use Cases

• Virtual Assistants: These are like digital helpers that understand what you say. They can do things like set reminders, search the internet, and control smart home devices, all without you having to touch anything. Examples include Siri , Alexa , and Google Assistant .
• Accessibility Tools: Speech recognition makes technology easier to use for people with disabilities. Features like voice control on phones and computers help them interact with devices more easily. There are also special apps for people with disabilities.
• Automotive Systems: In cars, you can use your voice to control things like navigation and music. This helps drivers stay focused and safe on the road. Examples include voice-activated navigation systems in cars.
• Healthcare: Doctors use speech recognition to quickly write down notes about patients, so they have more time to spend with them. There are also voice-controlled bots that help with patient care. For example, doctors use dictation tools to write down patient information quickly.
• Customer Service: Speech recognition is used to direct customer calls to the right place or provide automated help. This makes things run smoother and keeps customers happy. Examples include call centers that you can talk to and customer service bots .
• Education and E-Learning: Speech recognition helps people learn languages by giving them feedback on their pronunciation. It also transcribes lectures, making them easier to understand. Examples include language learning apps and lecture transcribing services.
• Security and Authentication: Voice recognition, combined with biometrics , keeps things secure by making sure it’s really you accessing your stuff. This is used in banking and for secure facilities. For example, some banks use your voice to make sure it’s really you logging in.
• Entertainment and Media: Voice recognition helps you find stuff to watch or listen to by just talking. This makes it easier to use things like TV and music services . There are also games you can play using just your voice.

Speech recognition is a powerful technology that lets computers understand and process human speech. It’s used everywhere, from asking your smartphone for directions to controlling your smart home devices with just your voice. This tech makes life easier by helping with tasks without needing to type or press buttons, making gadgets like virtual assistants more helpful. It’s also super important for making tech accessible to everyone, including those who might have a hard time using keyboards or screens. As we keep finding new ways to use speech recognition, it’s becoming a big part of our daily tech life, showing just how much we can do when we talk to our devices.

What is Speech Recognition?- FAQs

What are examples of speech recognition.

Note Taking/Writing: An example of speech recognition technology in use is speech-to-text platforms such as Speechmatics or Google’s speech-to-text engine. In addition, many voice assistants offer speech-to-text translation.

Is speech recognition secure?

Security concerns related to speech recognition primarily involve the privacy and protection of audio data collected and processed by speech recognition systems. Ensuring secure data transmission, storage, and processing is essential to address these concerns.

What is speech recognition in AI?

Speech recognition is the process of converting sound signals to text transcriptions. Steps involved in conversion of a sound wave to text transcription in a speech recognition system are: Recording: Audio is recorded using a voice recorder. Sampling: Continuous audio wave is converted to discrete values.

How accurate is speech recognition technology?

The accuracy of speech recognition technology can vary depending on factors such as the quality of audio input , language complexity , and the specific application or system being used. Advances in machine learning and deep learning have improved accuracy significantly in recent years.

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Speech Recognition with Wav2Vec2 ¶

Author : Moto Hira

This tutorial shows how to perform speech recognition using using pre-trained models from wav2vec 2.0 [ paper ].

The process of speech recognition looks like the following.

Extract the acoustic features from audio waveform

Estimate the class of the acoustic features frame-by-frame

Generate hypothesis from the sequence of the class probabilities

Torchaudio provides easy access to the pre-trained weights and associated information, such as the expected sample rate and class labels. They are bundled together and available under torchaudio.pipelines module.

Preparation ¶

Creating a pipeline ¶.

First, we will create a Wav2Vec2 model that performs the feature extraction and the classification.

There are two types of Wav2Vec2 pre-trained weights available in torchaudio. The ones fine-tuned for ASR task, and the ones not fine-tuned.

Wav2Vec2 (and HuBERT) models are trained in self-supervised manner. They are firstly trained with audio only for representation learning, then fine-tuned for a specific task with additional labels.

The pre-trained weights without fine-tuning can be fine-tuned for other downstream tasks as well, but this tutorial does not cover that.

We will use torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H here.

There are multiple pre-trained models available in torchaudio.pipelines . Please check the documentation for the detail of how they are trained.

The bundle object provides the interface to instantiate model and other information. Sampling rate and the class labels are found as follow.

Model can be constructed as following. This process will automatically fetch the pre-trained weights and load it into the model.

Loading data ¶

We will use the speech data from VOiCES dataset , which is licensed under Creative Commos BY 4.0.

To load data, we use torchaudio.load() .

If the sampling rate is different from what the pipeline expects, then we can use torchaudio.functional.resample() for resampling.

torchaudio.functional.resample() works on CUDA tensors as well.

When performing resampling multiple times on the same set of sample rates, using torchaudio.transforms.Resample might improve the performace.

Extracting acoustic features ¶

The next step is to extract acoustic features from the audio.

Wav2Vec2 models fine-tuned for ASR task can perform feature extraction and classification with one step, but for the sake of the tutorial, we also show how to perform feature extraction here.

The returned features is a list of tensors. Each tensor is the output of a transformer layer.

Feature classification ¶

Once the acoustic features are extracted, the next step is to classify them into a set of categories.

Wav2Vec2 model provides method to perform the feature extraction and classification in one step.

The output is in the form of logits. It is not in the form of probability.

Let’s visualize this.

We can see that there are strong indications to certain labels across the time line.

Generating transcripts ¶

From the sequence of label probabilities, now we want to generate transcripts. The process to generate hypotheses is often called “decoding”.

Decoding is more elaborate than simple classification because decoding at certain time step can be affected by surrounding observations.

For example, take a word like night and knight . Even if their prior probability distribution are differnt (in typical conversations, night would occur way more often than knight ), to accurately generate transcripts with knight , such as a knight with a sword , the decoding process has to postpone the final decision until it sees enough context.

There are many decoding techniques proposed, and they require external resources, such as word dictionary and language models.

In this tutorial, for the sake of simplicity, we will perform greedy decoding which does not depend on such external components, and simply pick up the best hypothesis at each time step. Therefore, the context information are not used, and only one transcript can be generated.

We start by defining greedy decoding algorithm.

Now create the decoder object and decode the transcript.

Let’s check the result and listen again to the audio.

The ASR model is fine-tuned using a loss function called Connectionist Temporal Classification (CTC). The detail of CTC loss is explained here . In CTC a blank token (ϵ) is a special token which represents a repetition of the previous symbol. In decoding, these are simply ignored.

Conclusion ¶

In this tutorial, we looked at how to use Wav2Vec2ASRBundle to perform acoustic feature extraction and speech recognition. Constructing a model and getting the emission is as short as two lines.

Total running time of the script: ( 0 minutes 6.845 seconds)

Download Python source code: speech_recognition_pipeline_tutorial.py

Download Jupyter notebook: speech_recognition_pipeline_tutorial.ipynb

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• Preparation
• Creating a pipeline
• Loading data
• Extracting acoustic features
• Feature classification
• Generating transcripts

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MelNet - Audio Samples

Audio samples accompanying the paper MelNet: A Generative Model for Audio in the Frequency Domain .

Single-Speaker Speech Generation

Multi-speaker speech generation, music generation, single-speaker text-to-speech, multi-speaker text-to-speech, wavenet baseline, ablation - multiscale modelling.

• Due to the large number of audio samples on this page, all samples have been compressed (96 kb/s mp3). The uncompressed files are available for download at this repository .
• Audio clips which correspond to ground-truth data are generated by inverting ground-truth spectrograms.
• Samples shown here were selected based on diversity and quality. Samples used for quantitative experiments in the paper were randomly drawn.

Samples generated by MelNet trained on the task of unconditional single-speaker speech generation using professionally recorded audiobook data from the Blizzard 2013 dataset.

Samples from the model without biasing or priming.

Biased Samples

Samples from the model using a bias of 1.0.

Primed Samples

The first 5 seconds of each audio clip are from the dataset and the remaining 5 seconds are generated by the model.

Samples generated by MelNet trained on the task of unconditional multi-speaker speech generation using noisy, multispeaker, multilingual speech data from the VoxCeleb2 dataset.

Samples generated by MelNet trained on the task of unconditional music generation using recorded piano performances from the MAESTRO dataset.

Samples generated by MelNet trained on the task of single-speaker TTS using professionally recorded audiobook data from the Blizzard 2013 dataset.

The first audio clip for each text is taken from the dataset and the remaining 3 are samples generated by the model.

“My dear Fanny, you feel these things a great deal too much. I am most happy that you like the chain,”

Looking with a half fantastic curiosity to see whether the tender grass of early spring,

“I like them round,” said Mary. “And they are exactly the color of the sky over the moor.”

Lydia was Lydia still; untamed, unabashed, wild, noisy, and fearless.

“Oh, he has been away from New York—he has been all round the world. He doesn't know many people here, but he's very sociable, and he wants to know every one.”

Each unlabelled audio clip is taken from the dataset and the audio clip that directly follows is a sample generated by the model primed with that sequence.

Write a fond note to the friend you cherish.

Pluck the bright rose without leaves.

Two plus seven is less than ten.

He said the same phrase thirty times.

We frown when events take a bad turn.

Samples generated by MelNet trained on the task of multi-speaker TTS using noisy speech recognition data from the TED-LIUM 3 dataset.

Samples generated by the model conditioned on text and speaker ID. The conditioning text and speaker IDs are taken directly from the validation set (text in the dataset is unnormalized and unpunctuated).

it wasn't like i was asking for the code to a nuclear bunker or anything like that but the amount of resistance i got from this

and what that form is modeling and shaping is not cement

that every person here every decision that you've made today every decision you've made in your life you've not really made that decision but in fact

syria was largely a place of tolerance historically accustomed

and no matter what the rest of the world tells them they should be

the years went by and the princess grew up into a beautiful young woman

i spent so much time learning this language why do i only

and we were down to eating one meal a day running from place to place but wherever we could help we did at a certain point in time in

phrases and words even if you have a phd of chinese language you can't understand them

and when they came back and told us about it we really started thinking about the ways in which we see styrofoam every day

is only a very recent religious enthusiasm it surfaced only in the west

chances are that they are rooted in the productivity crisis

i cannot face your fears or chase your dreams and you can't do that for me but we can be supportive of eachother

the first law of travel and therefore of life you're only as strong

Selected Speakers

Samples generated by the model for selected speakers. Reference audio for each of the speakers can be found on the TED website .

A cramp is no small danger on a swim.

The glow deepened in the eyes of the sweet girl.

Bring your problems to the wise chief.

Clothes and lodging are free to new men.

Port is a strong wine with a smoky taste.

For comparison, we train WaveNet on the same three unconditional audio generation tasks used to evaluate MelNet (single-speaker speech generation, multi-speaker speech generation, and music generation).

Samples without biasing or priming.

Samples with priming: 5 seconds from the dataset followed by 5 seconds generated by WaveNet.

Samples from a two-stage model which separately models MIDI notes and then uses WaveNet to synthesize audio conditioned on the generated MIDI notes.

The following models were trained on the same data, with each model using a different number of tiers.

5-Tier Model

4-tier model, 3-tier model, 2-tier model.

Top 11 Voice Recognition Applications in 2024

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

If you’ve used virtual assistants like Alexa, Cortana, Google Assistant, or Siri, you might be familiar with speech/voice recognition and conversational AI concepts. Speech recognition is the technology that enables users to explain themselves verbally to a device. It does that by converting the users’ verbal queries into machine-readable text.

​​As voice recognition technology might be used more than simply playing your favorite song on Spotify, its top 11 uses in marketing, customer service, healthcare, and other areas are reviewed in this article.

If you are interested in working with a voice/speech data collection service, click here.

Common applications

1. voice search.

Voice search is, arguably, the most common use of voice recognition. Reportedly, in 2022, in the US alone, 135.6M users would have used a digital assistant at least once a month.

Moreover, according to a PWC survey, using a voice assistant to search for something was the preferred method of 71% of participants (Figure 1). 

Source: PWC

Figure 1. 71% of the respondents preferred using their voice, rather than typing, to search for something online.

2. Speech to text

Voice recognition enables hands-free computing. Its use cases include, but are not limited to:

• Writing emails
• Composing a document on Google Docs
• Automatic closed captioning with speech recognition (i.e., YouTube)
• Automatic translation
• Sending texts ( Figure 2)

Figure 2. 58% of respondents claimed to text their friends using voice assistant rather than physically typing.

3. Voice commands to smart home devices

Smart home devices use voice recognition technology to carry out household tasks, such as turning on the lights, boiling water, adjusting the thermostat, and more.

Some statistics of smart home devices are:

• By 2025, the revenue of the market for smart home devices is projected to reach $182M .
• 30% of voice assistant users state smart home devices as their primary reason(s) for investing in an Amazon Echo or Google Home.
• By 2025, 478M households will have a smart home device.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

Business function applications

4. customer service.

Voice recognition is an important AI application in customer service . Voice recognition is an effective call center service solution, available 24/7, and is cheaper than live reps. 

The common use cases of speech recognition in customer service are:

• Interactive Voice Response (IVR): It is one of the oldest speech recognition applications and allows customers to reach the right agents or resolve their problems via voice commands.
• Analytics: Transcription of thousands of phone calls between customers and agents helps identify common call patterns and issues.

5. Pre-sales

Sales Development Reps (SDRs) calls can be wasteful. An example would be insurance companies calling their leads and asking them questions (i.e., their age, occupation, lifestyle, etc.) to see which insurance package they’d qualify for.

Such processes could be automated with voice bots. The benefit is that the caller will not have to wait to be connected with a sales rep. Rather, the bot will immediately start the evaluation and qualification process.

6. Voice biometrics for security

Similar to how your smartphone allows you to unlock it with your fingerprints, vocal biometrics uses a person’s speech to authenticate them. Users might be required to say their name aloud during log-ins rather than typing a password.

Alternatively, speech biometrics can be used in Fintech to authorize transactions and to guarantee they are genuine and consented from the account owner. Besides, speech biometrics can restrict access to authorized personnel in healthcare, where maintaining patient confidentiality is of utmost importance.

Industry applications

7. automotive.

In-car speech recognition systems have become a standard feature for most modern vehicles. Research has shown that by 2022, 73% of drivers will use an in-car voice assistant.

The biggest benefit of car speech recognition is that it allows the driver to keep their eyes on the road and hands on the wheels. Use cases include initiating phone calls, selecting radio stations, setting up directions, or playing music.

8. Academic

80% of sighted children’s learning is through vision, and their primary motivator is to explore the environment around them. Speech recognition can create an equitable learning platform for children with no/low sight. 

Language learning tools such as Duolingo use speech recognition to evaluate users’ language pronunciation. Pronunciation evaluation is a practical computer-aided language learning application.

9. Media/marketing

Speech recognition tools, such as dictation software, can enable people to write more words in less time. A study conducted by doctors using dictation software (Figure 3) showed that it produced 150 words per minute on average.

Source: Nuance

Figure 3. Doctors could write 150 words a minute using a dictation tool. 

So, approximately, content creators writing articles, speeches, books, memos, or emails can transcribe 3000-4000 words in 30 minutes using these applications. 

These tools aren’t 100% accurate, but they are good for first drafts. 

10. Healthcare

Md note-taking.

During patient examinations, doctors shouldn’t worry about taking notes of patients’ symptoms. Medical transcription (MD) software uses speech recognition to capture patient diagnosis notes. 

It’s been claimed that taking notes is one of the most time-consuming activities for physicians, taking their time away from seeing patients. Thanks to MD technology, doctors can shorten the average appointment duration and, in return, fit more patients into their schedules. 

Depression speech recognition technology listens to a patient’s voice to identify the existence, or lack thereof, of depression undertones through words such as “unhappy,” “overwhelmed,” “bored,” “feeling void,” etc. 

Vendors, such as Sonde Health, have created mobile applications that give users a score of “mental fitness” based on their voice’s tone, use of words, energy, fluctuations, and rhythm, among other variables.

11. Legal tech

Legal chatbots have grown in popularity because of their ease of use and wide applicability. Speech-enabled legal tech can expand the use cases to 

• Court reporting (Realtime Speech Writing)
• eDiscovery (Legal discovery)
• Automated transcripts in depositions and interrogations
• Using NLP to review legal documents to determine if they meet regulatory criteria.

You can also check our article to learn about speech recognition challenges and solutions .

For more on conversational AI

If you are interested in learning more about conversational AI, read:

• Speech Recognition: Everything You Need to Know
• Conversational User Interfaces
• 80+ Statistics on Conversational AI

And if you are looking for a voice bot platform, feel free to check our transparent vendor lists where sorting vendors by AIMultiple score shows the best solutions based on maturity, popularity, and satisfaction.

We will help you choose the right vendor for your business:

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

AIMultiple.com Traffic Analytics, Ranking & Audience , Similarweb. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics , Business Insider. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are , Washington Post. Data management barriers to AI success , Deloitte. Empowering AI Leadership: AI C-Suite Toolkit , World Economic Forum. Science, Research and Innovation Performance of the EU , European Commission. Public-sector digitization: The trillion-dollar challenge , McKinsey & Company. Hypatos gets $11.8M for a deep learning approach to document processing , TechCrunch. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million , Business Insider.

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Next to Read

Speech recognition: everything you need to know in 2024, top 5 speech recognition data collection methods in 2024, 4 ways to overcome deep learning challenges in 2024.

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Voice recognition tools are really helpful! As an alternative, I can recommend Audext. It works quite fast, and it has many useful features such as an in-built editor, text timings tracking, voice recognition in noise, etc.

SpeechRecognition 3.10.4

pip install SpeechRecognition Copy PIP instructions

Released: May 5, 2024

Library for performing speech recognition, with support for several engines and APIs, online and offline.

Verified details

Maintainers.

Unverified details

Project links, github statistics.

• Open issues:

View statistics for this project via Libraries.io , or by using our public dataset on Google BigQuery

License: BSD License (BSD)

Author: Anthony Zhang (Uberi)

Tags speech, recognition, voice, sphinx, google, wit, bing, api, houndify, ibm, snowboy

Requires: Python >=3.8

Classifiers

• 5 - Production/Stable
• OSI Approved :: BSD License
• MacOS :: MacOS X
• Microsoft :: Windows
• POSIX :: Linux
• Python :: 3
• Python :: 3.8
• Python :: 3.9
• Python :: 3.10
• Python :: 3.11
• Multimedia :: Sound/Audio :: Speech
• Software Development :: Libraries :: Python Modules

Project description

UPDATE 2022-02-09 : Hey everyone! This project started as a tech demo, but these days it needs more time than I have to keep up with all the PRs and issues. Therefore, I’d like to put out an open invite for collaborators - just reach out at me @ anthonyz . ca if you’re interested!

Speech recognition engine/API support:

Quickstart: pip install SpeechRecognition . See the “Installing” section for more details.

To quickly try it out, run python -m speech_recognition after installing.

Project links:

Library Reference

The library reference documents every publicly accessible object in the library. This document is also included under reference/library-reference.rst .

See Notes on using PocketSphinx for information about installing languages, compiling PocketSphinx, and building language packs from online resources. This document is also included under reference/pocketsphinx.rst .

You have to install Vosk models for using Vosk. Here are models avaiable. You have to place them in models folder of your project, like “your-project-folder/models/your-vosk-model”

See the examples/ directory in the repository root for usage examples:

First, make sure you have all the requirements listed in the “Requirements” section.

The easiest way to install this is using pip install SpeechRecognition .

Otherwise, download the source distribution from PyPI , and extract the archive.

In the folder, run python setup.py install .

Requirements

To use all of the functionality of the library, you should have:

The following requirements are optional, but can improve or extend functionality in some situations:

The following sections go over the details of each requirement.

The first software requirement is Python 3.8+ . This is required to use the library.

PyAudio (for microphone users)

PyAudio is required if and only if you want to use microphone input ( Microphone ). PyAudio version 0.2.11+ is required, as earlier versions have known memory management bugs when recording from microphones in certain situations.

If not installed, everything in the library will still work, except attempting to instantiate a Microphone object will raise an AttributeError .

The installation instructions on the PyAudio website are quite good - for convenience, they are summarized below:

PyAudio wheel packages for common 64-bit Python versions on Windows and Linux are included for convenience, under the third-party/ directory in the repository root. To install, simply run pip install wheel followed by pip install ./third-party/WHEEL_FILENAME (replace pip with pip3 if using Python 3) in the repository root directory .

PocketSphinx-Python (for Sphinx users)

PocketSphinx-Python is required if and only if you want to use the Sphinx recognizer ( recognizer_instance.recognize_sphinx ).

PocketSphinx-Python wheel packages for 64-bit Python 3.4, and 3.5 on Windows are included for convenience, under the third-party/ directory . To install, simply run pip install wheel followed by pip install ./third-party/WHEEL_FILENAME (replace pip with pip3 if using Python 3) in the SpeechRecognition folder.

On Linux and other POSIX systems (such as OS X), follow the instructions under “Building PocketSphinx-Python from source” in Notes on using PocketSphinx for installation instructions.

Note that the versions available in most package repositories are outdated and will not work with the bundled language data. Using the bundled wheel packages or building from source is recommended.

Vosk (for Vosk users)

Vosk API is required if and only if you want to use Vosk recognizer ( recognizer_instance.recognize_vosk ).

You can install it with python3 -m pip install vosk .

You also have to install Vosk Models:

Here are models avaiable for download. You have to place them in models folder of your project, like “your-project-folder/models/your-vosk-model”

Google Cloud Speech Library for Python (for Google Cloud Speech API users)

Google Cloud Speech library for Python is required if and only if you want to use the Google Cloud Speech API ( recognizer_instance.recognize_google_cloud ).

If not installed, everything in the library will still work, except calling recognizer_instance.recognize_google_cloud will raise an RequestError .

According to the official installation instructions , the recommended way to install this is using Pip : execute pip install google-cloud-speech (replace pip with pip3 if using Python 3).

FLAC (for some systems)

A FLAC encoder is required to encode the audio data to send to the API. If using Windows (x86 or x86-64), OS X (Intel Macs only, OS X 10.6 or higher), or Linux (x86 or x86-64), this is already bundled with this library - you do not need to install anything .

Otherwise, ensure that you have the flac command line tool, which is often available through the system package manager. For example, this would usually be sudo apt-get install flac on Debian-derivatives, or brew install flac on OS X with Homebrew.

Whisper (for Whisper users)

Whisper is required if and only if you want to use whisper ( recognizer_instance.recognize_whisper ).

You can install it with python3 -m pip install SpeechRecognition[whisper-local] .

Whisper API (for Whisper API users)

The library openai is required if and only if you want to use Whisper API ( recognizer_instance.recognize_whisper_api ).

If not installed, everything in the library will still work, except calling recognizer_instance.recognize_whisper_api will raise an RequestError .

You can install it with python3 -m pip install SpeechRecognition[whisper-api] .

Troubleshooting

The recognizer tries to recognize speech even when i’m not speaking, or after i’m done speaking..

Try increasing the recognizer_instance.energy_threshold property. This is basically how sensitive the recognizer is to when recognition should start. Higher values mean that it will be less sensitive, which is useful if you are in a loud room.

This value depends entirely on your microphone or audio data. There is no one-size-fits-all value, but good values typically range from 50 to 4000.

Also, check on your microphone volume settings. If it is too sensitive, the microphone may be picking up a lot of ambient noise. If it is too insensitive, the microphone may be rejecting speech as just noise.

The recognizer can’t recognize speech right after it starts listening for the first time.

The recognizer_instance.energy_threshold property is probably set to a value that is too high to start off with, and then being adjusted lower automatically by dynamic energy threshold adjustment. Before it is at a good level, the energy threshold is so high that speech is just considered ambient noise.

The solution is to decrease this threshold, or call recognizer_instance.adjust_for_ambient_noise beforehand, which will set the threshold to a good value automatically.

The recognizer doesn’t understand my particular language/dialect.

Try setting the recognition language to your language/dialect. To do this, see the documentation for recognizer_instance.recognize_sphinx , recognizer_instance.recognize_google , recognizer_instance.recognize_wit , recognizer_instance.recognize_bing , recognizer_instance.recognize_api , recognizer_instance.recognize_houndify , and recognizer_instance.recognize_ibm .

For example, if your language/dialect is British English, it is better to use "en-GB" as the language rather than "en-US" .

The recognizer hangs on recognizer_instance.listen ; specifically, when it’s calling Microphone.MicrophoneStream.read .

This usually happens when you’re using a Raspberry Pi board, which doesn’t have audio input capabilities by itself. This causes the default microphone used by PyAudio to simply block when we try to read it. If you happen to be using a Raspberry Pi, you’ll need a USB sound card (or USB microphone).

Once you do this, change all instances of Microphone() to Microphone(device_index=MICROPHONE_INDEX) , where MICROPHONE_INDEX is the hardware-specific index of the microphone.

To figure out what the value of MICROPHONE_INDEX should be, run the following code:

This will print out something like the following:

Now, to use the Snowball microphone, you would change Microphone() to Microphone(device_index=3) .

Calling Microphone() gives the error IOError: No Default Input Device Available .

As the error says, the program doesn’t know which microphone to use.

To proceed, either use Microphone(device_index=MICROPHONE_INDEX, ...) instead of Microphone(...) , or set a default microphone in your OS. You can obtain possible values of MICROPHONE_INDEX using the code in the troubleshooting entry right above this one.

The program doesn’t run when compiled with PyInstaller .

As of PyInstaller version 3.0, SpeechRecognition is supported out of the box. If you’re getting weird issues when compiling your program using PyInstaller, simply update PyInstaller.

You can easily do this by running pip install --upgrade pyinstaller .

On Ubuntu/Debian, I get annoying output in the terminal saying things like “bt_audio_service_open: […] Connection refused” and various others.

The “bt_audio_service_open” error means that you have a Bluetooth audio device, but as a physical device is not currently connected, we can’t actually use it - if you’re not using a Bluetooth microphone, then this can be safely ignored. If you are, and audio isn’t working, then double check to make sure your microphone is actually connected. There does not seem to be a simple way to disable these messages.

For errors of the form “ALSA lib […] Unknown PCM”, see this StackOverflow answer . Basically, to get rid of an error of the form “Unknown PCM cards.pcm.rear”, simply comment out pcm.rear cards.pcm.rear in /usr/share/alsa/alsa.conf , ~/.asoundrc , and /etc/asound.conf .

For “jack server is not running or cannot be started” or “connect(2) call to /dev/shm/jack-1000/default/jack_0 failed (err=No such file or directory)” or “attempt to connect to server failed”, these are caused by ALSA trying to connect to JACK, and can be safely ignored. I’m not aware of any simple way to turn those messages off at this time, besides entirely disabling printing while starting the microphone .

On OS X, I get a ChildProcessError saying that it couldn’t find the system FLAC converter, even though it’s installed.

Installing FLAC for OS X directly from the source code will not work, since it doesn’t correctly add the executables to the search path.

Installing FLAC using Homebrew ensures that the search path is correctly updated. First, ensure you have Homebrew, then run brew install flac to install the necessary files.

To hack on this library, first make sure you have all the requirements listed in the “Requirements” section.

To install/reinstall the library locally, run python -m pip install -e .[dev] in the project root directory .

Before a release, the version number is bumped in README.rst and speech_recognition/__init__.py . Version tags are then created using git config gpg.program gpg2 && git config user.signingkey DB45F6C431DE7C2DCD99FF7904882258A4063489 && git tag -s VERSION_GOES_HERE -m "Version VERSION_GOES_HERE" .

Releases are done by running make-release.sh VERSION_GOES_HERE to build the Python source packages, sign them, and upload them to PyPI.

To run all the tests:

To run static analysis:

To ensure RST is well-formed:

Testing is also done automatically by GitHub Actions, upon every push.

FLAC Executables

The included flac-win32 executable is the official FLAC 1.3.2 32-bit Windows binary .

The included flac-linux-x86 and flac-linux-x86_64 executables are built from the FLAC 1.3.2 source code with Manylinux to ensure that it’s compatible with a wide variety of distributions.

The built FLAC executables should be bit-for-bit reproducible. To rebuild them, run the following inside the project directory on a Debian-like system:

The included flac-mac executable is extracted from xACT 2.39 , which is a frontend for FLAC 1.3.2 that conveniently includes binaries for all of its encoders. Specifically, it is a copy of xACT 2.39/xACT.app/Contents/Resources/flac in xACT2.39.zip .

Please report bugs and suggestions at the issue tracker !

How to cite this library (APA style):

Zhang, A. (2017). Speech Recognition (Version 3.8) [Software]. Available from https://github.com/Uberi/speech_recognition#readme .

How to cite this library (Chicago style):

Zhang, Anthony. 2017. Speech Recognition (version 3.8).

Also check out the Python Baidu Yuyin API , which is based on an older version of this project, and adds support for Baidu Yuyin . Note that Baidu Yuyin is only available inside China.

Copyright 2014-2017 Anthony Zhang (Uberi) . The source code for this library is available online at GitHub .

SpeechRecognition is made available under the 3-clause BSD license. See LICENSE.txt in the project’s root directory for more information.

For convenience, all the official distributions of SpeechRecognition already include a copy of the necessary copyright notices and licenses. In your project, you can simply say that licensing information for SpeechRecognition can be found within the SpeechRecognition README, and make sure SpeechRecognition is visible to users if they wish to see it .

SpeechRecognition distributes source code, binaries, and language files from CMU Sphinx . These files are BSD-licensed and redistributable as long as copyright notices are correctly retained. See speech_recognition/pocketsphinx-data/*/LICENSE*.txt and third-party/LICENSE-Sphinx.txt for license details for individual parts.

SpeechRecognition distributes source code and binaries from PyAudio . These files are MIT-licensed and redistributable as long as copyright notices are correctly retained. See third-party/LICENSE-PyAudio.txt for license details.

SpeechRecognition distributes binaries from FLAC - speech_recognition/flac-win32.exe , speech_recognition/flac-linux-x86 , and speech_recognition/flac-mac . These files are GPLv2-licensed and redistributable, as long as the terms of the GPL are satisfied. The FLAC binaries are an aggregate of separate programs , so these GPL restrictions do not apply to the library or your programs that use the library, only to FLAC itself. See LICENSE-FLAC.txt for license details.

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Use These Employee Appreciation Speech Examples In 2024 To Show Your Team You Care

The simple act of saying “thank you” does wonders.

Yet sometimes, those two words alone don’t seem to suffice. Sometimes your team made such a difference, and your gratitude is so profound, that a pat on the back just isn’t enough.

Because appreciation is more than saying thank you . It’s about demonstrating that your team is truly seen and heard by thanking them for specific actions. It’s about showing that you understand and empathize with the struggles your team faces every day. And it’s about purpose too. True appreciation connects your team’s efforts back to a grand vision and mission.

According to Investopedia ,

“Appreciation is an increase in the value of an asset over time.”

So it’s time to diversify your portfolio of reliable tips and go-to words of wisdom for expressing your undying appreciation. After all, you diversify your portfolio of investments, and really, workplace appreciation is an investment.

Let’s set aside the standard definition of appreciation for a second and take a look at the financial definition.

In the workplace, appreciation increases the value of your most important assets—your employees—over time.

Here are some ways appreciation enhances employee relations:

• Appreciation makes employees stick around. In fact, statistics suggest that a lack of appreciation is the main driver of employee turnover , which costs companies an average of about $15,000 per worker .
• Appreciation reinforces employees’ understanding of their roles and expectations, which drives engagement and performance.
• Appreciation builds a strong company culture that is magnetic to both current and prospective employees.
• Appreciation might generate positive long-term mental effects for both the giver and the receiver.
• Appreciation motivates employees. One experiment showed that a few simple words of appreciation compelled employees to make more fundraising calls.

We searched through books, movies, songs, and even TED Talks to bring you 141 amazing motivational quotes for employees you’ll be proud to put in a Powerpoint, an intra-office meme or a foam board printing cutout! Find plenty of fantastic workplace quotes to motivate any team.

Some of the most successful entrepreneurs in American business built companies, and lasting legacies, by developing employees through the simple act of appreciation.

Charles Schwab, founder of the Charles Schwab Corporation, once said:

“I consider my ability to arouse enthusiasm among my people the greatest asset I possess, and the way to develop the best that is in a person is by appreciation and encouragement. There is nothing else that so kills the ambitions of a person as criticism from superiors. I never criticize anyone. I believe in giving a person incentive to work. So I am anxious to praise but loath to find fault. If I like anything, I am hearty in my appreciation and lavish in my praise.”

Boost your ability to arouse enthusiasm by learning how to deliver employee appreciation speeches that make an impact. Once you master the habits and rules below, sincere appreciation will flow from you like sweet poetry. Your employees are going to love it!

Page Contents (Click To Jump)

The Employee Appreciation Speech Checklist

Planning employee appreciation speeches can be fast and easy when you follow a go-to “recipe” that works every time. From a simple thank you to a heart felt work anniversary speech, it all has a template.

Maritz®studies human behavior and highlights relevant findings that could impact the workplace. They developed the Maritz Recognition Model to help everyone deliver the best appreciation possible. The model asserts that effective reward and recognition speech examples touch on three critical elements: the behavior, the effect, and the thank you.

Here’s a summary of the model, distilled into a checklist for your employee appreciation speeches:

• Talk about the behavior(s). While most employee appreciation speeches revolve around the vague acknowledgment of “hard word and dedication,” it’s best to call out specific actions and accomplishments so employees will know what they did well, feel proud, and get inspired to repeat the action. Relay an anecdote about one specific behavior to hook your audience and then expand the speech to cover everyone. You can even include appreciation stories from other managers or employees in your speech.
•  Talk about the effect(s) of the behavior(s). What positive effect did the employee behaviors have on your company’s mission? If you don’t have any statistics to share, simply discuss how you expect the behaviors to advance your mission.
•  Deliver the “thank you” with heartfelt emotion. Infusing speeches with emotion will help employees feel your appreciation in addition to hearing it. To pinpoint the emotional core of your speech, set the “speech” part aside and casually consider why you’re grateful for your employees. Write down everything that comes to mind. Which aspects made you tear up? Which gave you goosebumps? Follow those points to find the particular emotional way you want to deliver your “thank you” to the team .

Tips and tricks:

• Keep a gratitude journal (online or offline) . Record moments of workplace gratitude and employee acts you appreciate. This practice will make you feel good, and it also provides plenty of fodder for appreciation speeches or employee appreciation day .
• Make mini-speeches a habit. Try to deliver words of recognition to employees every single day. As you perfect small-scale appreciation speeches, the longer ones will also feel more natural.
• When speaking, pause frequently to let your words sink in.
• Making eye contact
• Controlling jittery gestures
• Acting out verbs
• Matching facial expression to words
• Moving around the stage
• Varied pace. Don’t drone on at the same pace. Speak quickly and then switch to speaking slowly.
• Varied volume. Raise your voice on key points and closings.

Employee Appreciation Speech Scripts

Build on these customizable scripts to deliver employee appreciation speeches and casual meeting shout-outs every chance you get. Each script follows the 3-step approach we discussed above. Once you get the hang of appreciation speech basics, you’ll be able to pull inspirational monologues from your hat at a moment’s notice.

Swipe the examples below, but remember to infuse each speech with your own unique perspectives, personality, and heartfelt emotions.

All-Purpose Appreciation Speech  

Greet your audience..

I feel so lucky to work with you all. In fact, [insert playful aside: e.g. My wife doesn’t understand how I don’t hate Mondays. It drives her nuts!]

Thanks to you, I feel lucky to come to work every day.

Talk about behaviors you appreciate.

Everyone here is [insert applicable team soft skills: e.g. positive, inspiring, creative, and intelligent ]. I’m constantly amazed by the incredible work you do.

Let’s just look at the past few months, for example. [Insert bullet points of specific accomplishments from every department].

• Finance launched an amazing new online payroll system.
• Business Development doubled their sales last quarter.
• Human Resources trained us all in emotional intelligence.

Talk about the effects of the behaviors.

These accomplishment aren’t just nice bullet points for my next presentation. Each department’s efforts has deep and lasting impacts on our business. [Explain the effects of each highlighted accomplishment].

• The new payroll system is going to save us at least $20,000 on staff hours and paper.
• Revenue from those doubled sales will go into our core investments, including a new training program .
• And I can already see the effects of that emotional intelligence training each time I’m in a meeting and a potential argument is resolved before it starts.

Say thank you.

I can’t thank you enough for everything you do for this company and for me. Knowing I have your support and dedication makes me a better, happier person both at work and at home.

Formal Appreciation Speech

Greet your audience by explaining why you were excited to come to work today..

I was not thrilled when my alarm went off this morning, but I must admit, I’m luckier than most people. As I got out of bed and thought about doing [insert daily workplace activities that inspire you], I felt excitement instead of dread. It’s an incredible feeling, looking forward to work every day, and for that, I have each and every one of you to thank.

Just last week, [insert specific anecdote: e.g. I remembered, ironically, that I forgot to create a real-time engagement plan for TECHLO’s giant conference next month. As you all know, they’re one of our biggest clients, so needless to say, I was panicking. Then I sit down for my one-on-one with MEGAN, worried that I didn’t even have time for our meeting, and what does she say? She wants to remind me that we committed to submit a promotional plan by the end of the week. She had some ideas for the TECHLO conference, so she went ahead and created a draft.]

[Insert the outcome of the anecdote: e.g. Her initiative dazzled me, and it saved my life! We met our deadline and also blew TECHLO away. In fact, they asked us to plan a similar initiative for their upcoming mid-year conference.]

[Insert a short thank-you paragraph tying everything together: e.g. And you know what, it was hard for me to pick just one example to discuss tonight. You all do so many things that blow me away every day. Thank you for everything. Thank you for making each day of work something we can all be proud of.]

Tip! Encourage your entire team to join in on the appreciation with CareCards ! This digital appreciation board allows you to recognize your colleague with a dedicated space full of personalized well wishes, thank-yous, and anything else you want to shout them out with! To explore Caroo’s CareCard program, take this 60-second tour !

Visionary Appreciation Speech

Greet your audience by explaining why you do what you do..

Here at [company name] we [insert core competency: e.g. build nonprofit websites], but we really [insert the big-picture outcome of your work: e.g. change the world by helping amazing nonprofits live up to their inspiring visions.]

I want to emphasize the “we” here. This company would be nothing without your work.

Talk about behaviors and explain how each works toward your mission.

Have you guys ever thought about that? How what you do [recap the big-picture outcome at your work: e.g. changes the world by helping amazing nonprofits live up to their inspiring visions]?

[Insert specific examples of recent work and highlight the associated outcomes: e.g. Let’s explore in terms of the websites we launched recently. I know every single person here played a role in developing each of these websites, and you should all be proud.]

• The launch of foodangel.org means that at least 500 homeless people in the greater metro area will eat dinner tonight.
• The launch of happyup.org means thousands of depressed teenagers will get mental health counseling.

Now if that’s not [recap the big-picture outcome], then I don’t know what is.

Thank you for joining me on the mission to [big-picture outcome]. With any other team, all we’re trying to do might just not be possible, but you all make me realize we can do anything together.

Casual Appreciation Speech

Greet your audience by discussing what upcoming work-related items you are most excited about..

I’ve been thinking nonstop about [insert upcoming initiative: e.g. our upcoming gallery opening]. This [initiative] is the direct result of your amazing work. To me, this [initiative] represents [insert what the initiative means to you: e.g. our true debut into the budding arts culture of our city.]

You’ve all been pulling out all the stops, [insert specific example: e.g. staying late, making 1,000 phone calls a day, and ironing out all the details.]

Because of your hard work, I’m absolutely confident the [initiative] will [insert key performance indicator: e.g. sell out on opening night.]  

Thank you, not just for making this [initiative] happen, but also for making the journey such a positive and rewarding experience.

Funny Appreciation Speech

Greet your audience by telling an inside joke..

I want to thank you all for the good times, especially [insert inside joke: e.g. that time we put a glitter bomb in Jeff’s office.]

Talk about behaviors you appreciate and highlight comical outcomes.

But seriously, you guys keep me sane. For example [insert comical examples: e.g.]:

• The Operations team handled the merger so beautifully, I only had to pull out half my hair.
• The Marketing team landed a new client, and now we can pay you all for another year.
• And thanks to the Web team’s redesign of our website, I actually know what we do here.

Talk about the real effects of the behaviors.

But for real for real, all your work this year has put us on a new level. [Insert outcomes: e.g. We have an amazing roster of clients, a growing staff, and an incredible strategic plan that makes me feel unqualified to work here.] You guys made all this happen.

So thank you. This is when I would usually tell a joke to deflect my emotions, but for once in my life, I actually don’t want to hide. I want you all to know how much I appreciate all you do.

That was hard; I’m going to sit down now.

Appreciation Speech for Employee of the Month

Greet your audience by giving a shout-out to the employee of the month..

Shout out to [insert employee’s name] for being such a reliable member of our team. Your work ethics and outstanding performance are an inspiration to all of us! Keep up the amazing work!

Talk about behaviors you appreciate in them and highlight their best traits.

It’s not only essential to work diligently, but it is likewise crucial to be kind while you’re at it–and you’ve done both wonderfully!

Talk about the effects of their behaviors on the success of the company.

You bring optimism, happiness, and an all-around positive attitude to this team.

Thank you bring you!

Appreciation Speech for Good Work

Greet your audience with a round of applause to thank them for their hard work..

You always put in 100% and we see it. Proud of you, team!

Talk about behaviors you appreciate in your team members.

You work diligently, you foster a positive team environment, and you achieve or exceed your goals. 

Talk about the effects of your team’s behaviors on the company.

Your dedication to the team is commendable, as is your readiness to do whatever needs to be done for the company – even if it’s not technically part of your job description. Thank you.

No matter the situation, you always rise to the occasion! Thank you for your unwavering dedication; it doesn’t go unnoticed.

People Also Ask These Questions:

Q: how can i show that i appreciate my employees .

• A: An appreciation speech is a great first step to showing your employees that you care. The SnackNation team also recommends pairing your words of appreciation with a thoughtful act or activity for employees to enjoy. We’ve researched, interviewed, and tested all the best peer-to-peer recognition platforms, office-wide games, celebration events, and personalized rewards to bring you the   top 39 recognition and appreciation ideas to start building a culture of acknowledgment in your office.

Q: What should I do after giving an appreciation speech? 

• A: In order to drive home the point of your employee appreciation speech, it can be effective to reward your employees for their excellent work. Rewards are a powerful tool used for employee engagement and appreciation. Recognizing your employees effectively is crucial for retaining top talent and keeping employees happy. To make your search easier, we sought out the top 121 creative ways that companies can reward their employees that you can easily implement at your office.

Q: Why should I give an employee appreciation speech? 

• A: Appreciation and employee motivation are intimately linked together. A simple gesture of an employee appreciation gift can have a positive effect on your company culture. When an employee is motivated to work they are more productive. For more ideas to motivate your team, we’ve interviewed leading employee recognition and engagement experts to curate a list of the 22 best tips here ! 

We hope adapting these tips and scripts will help you articulate the appreciation we know you already feel!

Free Download:   Download this entire list as a PDF . Easily save it on your computer for quick reference or print it for future team gatherings.

Employee Recognition & Appreciation Resources:

39 impactful employee appreciation & recognition ideas [updated], 12 effective tools & strategies to improve teamwork in the workplace, your employee referral program guide: the benefits, how-tos, incentives & tools, 21 unforgettable work anniversary ideas [updated], 15 ideas to revolutionize your employee of the month program, 16 awesome employee perks your team will love, 71 employee recognition quotes every manager should know, how to retain employees: 18 practical takeaways from 7 case studies, boost your employee recognition skills and words (templates included).

Interested in a content partnership? Let’s chat!

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10 Comments

great piece of work love it, great help, thanks.

great tips !!!!

Helpful piece. LAVISH MAYOR

Enjoy reading this. Nice work

Thank you. Very helpful tips.

This is the most helpful and practical article I have found for writing a Colleague Appreciation speech. The Funny Appreciation Speech section was written for me 🙂 Ashley Bell, you’re a rock star!

Very nice speech Well explanation of words And very helpful for work

Hi, Your notes are awesome. Thank you for the share.

Your article is very helpful. Thankyou :).

Your stuff is really awesome. Thankyou for sharing such nice information

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How to Write a Meaningful Appreciation Speech

Updated 09/9/2022

Published 06/15/2020

Kate Wight, BA in English

Contributing writer

Cake values integrity and transparency. We follow a strict editorial process to provide you with the best content possible. We also may earn commission from purchases made through affiliate links. As an Amazon Associate, we earn from qualifying purchases. Learn more in our affiliate disclosure .

There are many ways to show someone that you appreciate them. You can buy them a gift. You can write them a thank-you note. And in some cases, you can give a speech in their honor. There are plenty of occasions when you may find yourself in a position to give an appreciation speech.

Jump ahead to these sections: 

• Steps for Writing an Appreciation Speech

Sample Appreciation Speeches

If you’re graduating from high school or college, you might give a speech thanking friends and family members for their support.

If you own a business, you might tell your employees “ thank you for your support ” as part of a speech. Speeches can also be a great way to say “ I appreciate you ” to the people in your life who support you.

Here, we break down the steps that go into crafting an excellent appreciation speech. We also include excerpts of speeches from an assortment of occasions and audiences to draw inspiration from. 

Step 1: Know Your Audience — And Your Place

Your speech will depend on a variety of factors. But the most important ones to consider are the setting and the crowd. If your speech is a casual toast between friends over a bottle of wine, it will be a lot more casual.

You can rely on personal anecdotes and the language you use will be more personal. If you’re giving a formal speech in front of colleagues though, your tone will be very different. Your speech will be a lot more structured and concise. 

Step 2: Create an Outline

Whether your appreciation speech is long or short, it’s always a good idea to craft an outline ahead of time. This will help you make sure you don’t forget to mention anything you want to cover. Overall, most speeches will break down like the following:

• Introduction: In an introduction, you will let the audience know who you are and give a preface of what you plan to say. For instance, if you’re recognizing a specific person in an appreciation speech, give a quick rundown of why they’re worthy of appreciation.
• Body: Here, you’ll flesh out the points you made in your introduction. You can give more specific examples of things the subject of your speech has done, and you’ll expand on why those actions deserve gratitude. 
• Conclusion: In this final section, you can reiterate the points you made earlier in the speech. 

Step 3: Grab People’s Attention with Gratitude

Start with a strong opening line. In a more formal speech, a quote about gratitude can be an excellent way to set the tone. In a more casual speech, you can avoid a quote. However, you should still stick with the theme of gratitude.   

Step 4: Be Personal and Specific

In casual and formal speeches alike, you should feel free to be specific. If you’re giving a speech in honor of one person, you can list all of the things they do that deserve appreciation. If you’re thanking other people for their support, you can list the ways they helped you.

Personal anecdotes are a lot more engaging for listeners. They will also help you feel more connected to your material. The more connected you feel, the more confident you’ll be in speaking. These personal anecdotes can be funny, poignant, or a blend of the two. Again, this will largely be dictated by your audience and the setting of your speech.    

Step 5: Practice Makes Perfect

For a casual speech like an appreciation toast, you can probably get away with speaking off the cuff. But any kind of pre-planned appreciation speech definitely benefits from repeated practice.

The more comfortable you are with the speech, the easier it will be for you to deliver it. If you don’t know your speech inside and out, there’s a good chance that you can be tripped up by certain words or turns of phrase. 

Step 6: Time Yourself

When you’re practicing your speech, you should also be timing yourself. This means you should have a stopwatch going while you read your speech aloud. Speeches can be deceptive.

A few pages don’t seem like they should take that long to read. If you only read them over in your head, that can reinforce the notion that your speech isn’t that long. But it takes a lot longer to read something aloud than it does reading it to yourself.

If you don’t practice it out loud ahead of time, you may panic in the middle of your actual delivery. If you fear your speech is taking too long, you might start to read faster and faster, which could make the speech incomprehensible. Practicing it out loud can help you hit your ideal time target without having to rush.   

Step 7: Keep Your Notes Handy

Even if you’ve practiced your speech until it’s practically etched into your brain, you always want to keep notes or an outline with you. No matter how much you practice, you may find yourself freezing up in the moment. If you don’t have notes handy, you might flounder. On the other hand though, you also shouldn’t keep your whole speech with you.

If you do, you might find yourself relying on it like a security blanket. You may end up just reading the whole speech straight from the paper without engaging with your audience at all. Both ends of the spectrum are too extreme, so it’s best to find a happy medium. Some people just keep their outline with them.

Other people write out the first sentence of each paragraph to jog their memory and help them orient themselves. As you practice, you’ll find the method that works best for you.

Step 8: Do a Test Run in Front of an Audience

Practicing for a speech on your own is important. But once you feel more comfortable with the speech, you should practice in front of someone. Ideally, you’ll rehearse it in front of people several times until you can keep your nervous responses in check.

This means delivering the speech without your heart racing and your speech speeding up to match.   

Step 9: Weed Out Any Trouble Spots

Every time you practice your speech, you should be refining it until you can’t improve it any further. One of the big things you should be looking out for is your usage of filler words or speech disfluencies.

Speech disfluencies encompass those little noises like “um”, “er”, and “uh” that we tend to use when we aren’t confident. These can make people tune out because your discomfort makes them feel awkward in turn.

As you practice, pay attention to places where you’re inserting those disfluencies. Keep practicing them until you become comfortable enough to leave them out. Or, rewrite those sections so they come to you more naturally.  

Step 10: End On a Good Note

Above all else, remember that this speech is intended to be a positive thing. An appreciation speech should make someone’s day .

Remember to end the speech by reaffirming specifically why you are showing appreciation.    

Now that we’ve gone into what makes a good appreciation speech, let’s see some examples. These are just excerpts from longer speeches, but they may help demonstrate the sort of content you might be looking for. 

Example of appreciation speech for graduation

“As I look around at all my classmates, I realize how much I appreciate you all. Many of us have relied on each other to make it through school and to our graduation day. We supported each other during tough times. We used each other’s examples to fuel us towards getting better grades. When someone was in danger of not graduating, we pulled together to get everyone to the finish line. We all owe a lot to our families for their support. But we should also be sure to appreciate ourselves.” 

Example of appreciation speech for friends

“I’d like to take a moment to raise a glass in appreciation for Bethany. Everyone here has one thing in common — Bethany’s friendship. She has always had an uncanny knack for finding people in need of a community and bringing us together. From there, we’ve been able to find the other things that connect us. But if it weren’t for Bethany, most of us would have missed out on enriching, life-changing friendships. Bethany — here’s to you!”

Example of appreciation speech for employees or a boss

“As the year draws to an end, I’m proud to announce that it’s the company’s strongest year yet. We have grown by leaps and bounds and still managed to maintain profitability. Our client satisfaction scores have never been higher. And each and every one of you has played a role in our success.

"I want to thank our sales division for going above and beyond in meeting our clients’ needs. I want to thank our marketing department for creating materials that are very transparent about our mission. I want to thank the managers for leading their divisions by example. I could stand up here and tell you a half dozen things I appreciate about every person in this room, but I’m sure you’re all ready to hit the buffet line. So I’ll conclude by saying that I appreciate all of your contributions, and am so proud to be on a team with each and every one of you.”

Example of appreciation speech for mom, dad, grandma, or grandpa

“Hello everyone! I’d like to thank all of you for coming here today in celebration of Grandma Joy and Grandpa Bill’s 50th wedding anniversary. As most of you know, I’ve never had a relationship last more than a year. Fifty years is an absolutely mind-boggling level of commitment to someone like me. 

"There are so many reasons to love and appreciate Joy and Bill. There’s all the basic grandparent stuff. Joy taught me how to make amazing cookies and Bill taught me how to change a tire. But they also took me in when my home life was less than ideal. And when they realized some of my friends also had difficult lives at home, they opened up their den as a safe space. On any given day, you could find at least two or three misfit teenagers sleeping on their fold-out couch. 

"Grandma and Grandpa, I’ll never be able to let you know how much I appreciate you. I know you probably don’t think you even did anything special. But you have made so many lives worth living thanks to your compassion and generosity. Thank you for always being there for others, just like you’ve been there for each other for five decades.”

Show People You Appreciate Them Through Meaningful Speeches

There are many ways to show gratitude . An appreciation speech is just one of them. Whether you’re giving a short toast or a lengthy speech, you can communicate your gratitude for someone.

These steps and examples should help you craft an excellent speech. Ultimately though, just remember to be sincere and personal. That’s the real key to successfully showing appreciation. 

Categories:

• Condolences & What To Say

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SpeechRecognition

The SpeechRecognition interface of the Web Speech API is the controller interface for the recognition service; this also handles the SpeechRecognitionEvent sent from the recognition service.

Note: On some browsers, like Chrome, using Speech Recognition on a web page involves a server-based recognition engine. Your audio is sent to a web service for recognition processing, so it won't work offline.

Constructor

Creates a new SpeechRecognition object.

Instance properties

SpeechRecognition also inherits properties from its parent interface, EventTarget .

Returns and sets a collection of SpeechGrammar objects that represent the grammars that will be understood by the current SpeechRecognition .

Returns and sets the language of the current SpeechRecognition . If not specified, this defaults to the HTML lang attribute value, or the user agent's language setting if that isn't set either.

Controls whether continuous results are returned for each recognition, or only a single result. Defaults to single ( false .)

Controls whether interim results should be returned ( true ) or not ( false .) Interim results are results that are not yet final (e.g. the SpeechRecognitionResult.isFinal property is false .)

Sets the maximum number of SpeechRecognitionAlternative s provided per result. The default value is 1.

Instance methods

SpeechRecognition also inherits methods from its parent interface, EventTarget .

Stops the speech recognition service from listening to incoming audio, and doesn't attempt to return a SpeechRecognitionResult .

Starts the speech recognition service listening to incoming audio with intent to recognize grammars associated with the current SpeechRecognition .

Stops the speech recognition service from listening to incoming audio, and attempts to return a SpeechRecognitionResult using the audio captured so far.

Listen to these events using addEventListener() or by assigning an event listener to the oneventname property of this interface.

Fired when the user agent has started to capture audio. Also available via the onaudiostart property.

Fired when the user agent has finished capturing audio. Also available via the onaudioend property.

Fired when the speech recognition service has disconnected. Also available via the onend property.

Fired when a speech recognition error occurs. Also available via the onerror property.

Fired when the speech recognition service returns a final result with no significant recognition. This may involve some degree of recognition, which doesn't meet or exceed the confidence threshold. Also available via the onnomatch property.

Fired when the speech recognition service returns a result — a word or phrase has been positively recognized and this has been communicated back to the app. Also available via the onresult property.

Fired when any sound — recognizable speech or not — has been detected. Also available via the onsoundstart property.

Fired when any sound — recognizable speech or not — has stopped being detected. Also available via the onsoundend property.

Fired when sound that is recognized by the speech recognition service as speech has been detected. Also available via the onspeechstart property.

Fired when speech recognized by the speech recognition service has stopped being detected. Also available via the onspeechend property.

Fired when the speech recognition service has begun listening to incoming audio with intent to recognize grammars associated with the current SpeechRecognition . Also available via the onstart property.

In our simple Speech color changer example, we create a new SpeechRecognition object instance using the SpeechRecognition() constructor, create a new SpeechGrammarList , and set it to be the grammar that will be recognized by the SpeechRecognition instance using the SpeechRecognition.grammars property.

After some other values have been defined, we then set it so that the recognition service starts when a click event occurs (see SpeechRecognition.start() .) When a result has been successfully recognized, the result event fires, we extract the color that was spoken from the event object, and then set the background color of the <html> element to that color.

Specifications

Browser compatibility

BCD tables only load in the browser with JavaScript enabled. Enable JavaScript to view data.

• Web Speech API

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File metadata and controls, wav2vec 2.0.

wav2vec 2.0 learns speech representations on unlabeled data as described in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020) .

We learned speech representations in multiple languages as well in Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020) .

We also combined wav2vec 2.0 with self-training in Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020) .

We combined speech data from multiple domains in Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021) .

We finetuned XLSR-53 on multiple languages to transcribe unseen languages in Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021) .

Pre-trained models

* updated (Oct. 24, 2020) ** updated (Nov. 13, 2021)

We also release multilingual pre-trained wav2vec 2.0 (XLSR) models:

The XLSR model uses the following datasets for multilingual pretraining:

MLS: Multilingual LibriSpeech (8 languages, 50.7k hours): Dutch, English, French, German, Italian, Polish, Portuguese, Spanish

CommonVoice (36 languages, 3.6k hours): Arabic, Basque, Breton, Chinese (CN), Chinese (HK), Chinese (TW), Chuvash, Dhivehi, Dutch, English, Esperanto, Estonian, French, German, Hakh-Chin, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Mongolian, Persian, Portuguese, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Welsh (see also finetuning splits from this paper ).

Babel (17 languages, 1.7k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu

We also finetuned several models on languages from CommonVoice (version 6.1) and Babel . Please refer to our paper for details about which languages are used.

We release 2 models that are finetuned on data from 2 different phonemizers. Although the phonemes are all IPA symbols, there are still subtle differences between the phonemized transcriptions from the 2 phonemizers. Thus, it's better to use the corresponding model, if your data is phonemized by either phonemizer above.

Training a new model with the CLI tools

Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length)

Prepare training data manifest

First, install the soundfile library:

$ext should be set to flac, wav, or whatever format your dataset happens to use that soundfile can read.

$valid should be set to some reasonable percentage (like 0.01) of training data to use for validation. To use a pre-defined validation set (like dev-other from librispeech), set to it 0 and then overwrite valid.tsv with a separately pre-processed manifest file.

Train a wav2vec 2.0 base model

This configuration was used for the base model trained on the Librispeech dataset in the wav2vec 2.0 paper

Note that the input is expected to be single channel, sampled at 16 kHz

Note: you can simulate 64 GPUs by using k GPUs and adding command line parameters (before --config-dir ) distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 64/k

Train a wav2vec 2.0 large model

This configuration was used for the large model trained on the Libri-light dataset in the wav2vec 2.0 paper

Note: you can simulate 128 GPUs by using k GPUs and adding command line parameters (before --config-dir ) distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 128/k

Train a wav2vec 2.0 model with conformer backbone

To replace the transformer layers in the encoder with the conformer layers, set --layer-type conformer --attn-type espnet --pos-enc-type ${POS_ENC_TYPE} . POS_ENC_TYPE refers to positional encoding to be used in the conformer encoder. Set it to abs , rope or rel_pos to use the absolute positional encoding, rotary positional encoding or relative positional encoding in the conformer layer respectively.

To train a base model with conformer:

To train a large model with conformer:

Fine-tune a pre-trained model with CTC

Fine-tuning a model requires parallel audio and labels file, as well as a vocabulary file in fairseq format. A letter vocabulary can be downloaded here . An example script that generates labels for the Librispeech dataset from the tsv file produced by wav2vec_manifest.py can be used as follows:

Fine-tuning on 100h of Librispeech with letter targets:

There are other config files in the config/finetuning directory that can be used to fine-tune on other splits. You can specify the right config via the --config-name parameter.

Note: you can simulate 24 GPUs by using k GPUs and adding command line parameters (before --config-dir ) distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 24/k

Decoding with a language model during training requires flashlight python bindings (previously called wav2letter . If you want to use a language model, add +criterion.wer_args='[/path/to/kenlm, /path/to/lexicon, 2, -1]' to the command line.

Evaluating a CTC model

Evaluating a CTC model with a language model requires flashlight python bindings (previously called wav2letter to be installed.

Fairseq transformer language model used in the wav2vec 2.0 paper can be obtained from the wav2letter model repository . Be sure to upper-case the language model vocab after downloading it.

Letter dictionary for pre-trained models can be found here .

Next, run the evaluation command:

To get raw numbers, use --w2l-decoder viterbi and omit the lexicon. To use the transformer language model, use --w2l-decoder fairseqlm.

Use wav2vec 2.0 with 🤗Transformers

Wav2Vec2 is also available in the 🤗Transformers library since version 4.4.

Pretrained Models can be found on the hub and documentation can be found here .

Usage example:

Example to train a wav2vec model as described in wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019) .

Example usage

Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate files 10 to 30 seconds in length)

Train a wav2vec model

Run wav2vec2 pre-training on google cloud tpus.

Wav2Vec2 is now supported on TPUs! It's currently pre-training only.

Using hydra on a v3-8

Using command line arguments on a v3-8.

Note: Commandline arguments way of execution has a known-problem currently.

Using hydra on a pod slice (v3-N with N > 8)

Using command line arguments on a pod slice (v3-n with n > 8), extract embeddings from the downstream task data.

Example to train a vq-wav2vec model as described in vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations (Baevski et al., 2019) .

These models are also used in Effectiveness of self-supervised pre-training for speech recognition (Baevski et al., 2019) .

Train a gumbel vq-wav2vec model

for k-means training, set vq-type with "kmeans" and add --loss-weights [1] argument. Pre-trained models were trained on 16 GPUs.

Tokenize audio data (e.g. for BERT training)

Closing Remarks Speech for Recognition Day

Ai generator.

Ladies and Gentlemen,

Distinguished guests, faculty, parents, and dear students,

Thank you all for being here today to celebrate Recognition Day, a special occasion where we acknowledge the hard work, dedication, and achievements of our students. It has been a wonderful event, filled with joy, pride, and inspiration.

To the students : Today is a testament to your perseverance, commitment, and excellence. You have worked hard, overcome challenges, and strived for success. Your achievements are a source of pride not only for you but also for your families, teachers, and the entire school community. Remember that this recognition is just one step in your journey. Continue to strive for excellence, embrace new challenges, and never stop learning.

To the teachers and staff : Your unwavering dedication and support have been instrumental in guiding these students to success. Thank you for your hard work, patience, and passion for education. Your efforts have made a significant difference in the lives of these students.

To the parents and families : Your support, encouragement, and sacrifices have played a crucial role in your children’s achievements. Thank you for being their constant source of motivation and strength.

To our guests and supporters : Your presence and contributions have added immense value to this event. We are grateful for your support and involvement in our school community.

As we conclude this celebration, let’s reflect on the achievements we have recognized today and look forward to the future with hope and determination. May this day inspire all of us to continue working hard, supporting each other, and striving for greatness.

Congratulations to all the honorees! You have made us proud, and we look forward to seeing all the amazing things you will accomplish in the future.

Thank you once again for your participation and support. Have a wonderful day, and see you at future events.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Read Aloud: A Text to Speech Voice Reader

Read aloud the current web-page article with one click, using text to speech (TTS). Supports 40+ languages.

Read Aloud uses text-to-speech (TTS) technology to convert webpage text to audio. It works on a variety of websites, including news sites, blogs, fan fiction, publications, textbooks, school and class websites, and online university course materials. Read Aloud allows you to select from a variety of text-to-speech voices, including those provided natively by the browser, as well as by text-to-speech cloud service providers such as Google Wavenet, Amazon Polly, IBM Watson, and Microsoft. Some of the cloud-based voices may require additional in-app purchase to enable. Read Aloud can read PDF, Google Docs, Google Play books, Amazon Kindle, and EPUB (via the excellent EPUBReader extension from epubread.com). Read Aloud is intended for users who prefer to listen to content instead of reading, those with dyslexia or other learning disabilities, and children learning to read. To use Read Aloud, navigate to the web page you want to read, then click the Read Aloud icon on the Chrome menu. In addition, the shortcut keys ALT-P, ALT-O, ALT-Comma, and ALT-Period can be used to Play/Pause, Stop, Rewind, and Forward. You may also select the text you want to read before activating the extension. Right clicking on the selected text will provide you with yet another option to activate Read Aloud via the context menu. To change the voice, reading speed, pitch, or enable text highlighting, go to the Options page either by right clicking on the Read Aloud icon and choose Options, or by clicking the Gear button on the extension popup (you'll need to stop playback to see the Gear button). Read Aloud is an open-source project. If you wish to contribute bug fixes or translations, please visit the GitHub page at https://github.com/ken107/read-aloud.

4.2 out of 5 3K ratings Google doesn't verify reviews. Learn more about results and reviews.

Chris Wisner Jun 9, 2024

how do I speed up the read aloud feature? I want 1.5 / 2.0 speed please

Beau Brewer Jun 6, 2024

The price is right! Free and opensource that can use my own APIs! This is just what I was looking for! Thanks lsdsoftware!

Read Aloud: A Text to Speech Voice Reader handles the following:

This developer declares that your data is.

• Not being sold to third parties, outside of the approved use cases
• Not being used or transferred for purposes that are unrelated to the item's core functionality
• Not being used or transferred to determine creditworthiness or for lending purposes

Pericles: Text to Speech Screen Reader

Speech synthesis redesigned. Listen to emails, documents & websites.

Speak Any Text

Select any text you want to read and just listen to it!

Page Reader

A completely free extension to read highlighted text via Text To Speech.

TTS Ebook Reader

Supports Kindle,Google Play,Scribd,Overdrive and Gutenberg, powered by Google TTS (Text to Speech), turns ebooks into audible books

Speechify Text to Speech Voice Reader

Read aloud any Google Doc, PDF, webpage, or book with text to speech (TTS). Natural sounding voices in 30+ languages & 130 voices.

TTS Text To Speech - Voice Reader Online

AI-powered text-to-speech tool. Voice over for books and PDF files. ChatGPT summarizer for anything.

NaturalReader - AI Text to Speech

Read aloud any text with realistic AI voices, compatible with webpages, kindle Ebooks, Google Docs, PDF, Emails, and more.

Readme - Text to Speech

Readme - Text to Speech can read aloud text from any websites, eBooks and documents. Simply select and speak (TTS).

Talking Web

Select the text and let Talking Web read it for you.

Talkie: text-to-speech, many languages!

Fast, easy, high-quality text to speech in over 40 languages. Read out loud from websites, PDF, email. Speak text with TTS.

Select and Speak - Text to Speech

Select and Speak uses iSpeech’s human-quality text-to-speech (TTS) to read any selected text in the browser. It includes many…

Text to Speech

Text to Speech Hewizo removes ads and reads articles in over 30+ languages using state of the art AI text to speech engine TTS

Text to speech

Create lifelike voiceovers to suit any video with the ai text to speech generator..

Professional voiceover features

Diverse array of realistic voices.

Choose from hundreds of natural sounding voices in neutral, feminine, and masculine tones, multilingual, and AI generated sounds. 

Extensive variety of languages

Select from over 80 different languages like Spanish, Japanese, Hindi, Italian, Arabic, German, French, and many more .

Personalize the pitch and pace

Customize the pitch of your AI voice from extra low, low, medium, high, and extra high. The AI text to speech generator allows you to choose from 0.5x speed to 2x speed for free.

How to use text to speech in Clipchamp

Click on the text to speech generator

Pick a language, voice, pitch and pace

Enter your text to generate a preview

Save to the editing timeline

Ideal for creators

Capture attention faster on social media  

Make YouTube tutorial videos easy to follow  

Create funny gaming highlights with AI voiceovers  

Perfect for businesses

Create consistent corporate presentation videos  

Reimagine culture videos with AI narration  

Refine training videos and screen recordings   

Excellent for online learning

Make inclusive and accessible videos with voiceovers 

Create informative lesson plan highlights  

Drive more engaging virtual learning content  

Voiceover writing tips 

Unlock AI text to speech's full potential and produce natural sounding voiceovers for your videos by adjusting the pace and intonation of your narration. 

Full stops add a moderate pause to your text to speech. 

Commas add a short pause to your text to speech. 

Ellipses (“ …”) add a long pause in your voiceover.  

Question marks change the intonation of your voiceover.  

Exclamation marks and typing in all caps don't change the delivery of your text to speech. 

Need more help creating AI voiceovers for videos?

Head over to our helpful text to speech guide and take a look at the video tutorial for more AI voiceover tips and tricks. 

Read our related blogs

Video accessibility checklist: make inclusive videos with Clipchamp

How to make your own TikTok voiceover with Clipchamp

How to make a product demo video voiceover with AI

Frequently asked questions , is the text to speech generator free in clipchamp.

Yes. Clipchamp is an easy online video editor that lets you turn your text into a realistic AI voiceover for free. 

What languages is the AI voiceover generator available in?

The AI text to speech generator is available in the following languages : Arabic, Albanian, Armenian, Azerbaijani, Afrikaans, Amharic, Bulgarian, Burmese, Basque, Bosnian, Bengali, Bangla, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Filipino, Finnish, Persian, German, Georgian, Greek, Gujarati, Galician, Hebrew, Hindi, Hungarian, Indonesian, Icelandic, Irish, Italian, Japanese, Javanese, Kazakh, Khmer, Kannada, Korean, Lao, Latvian, Lithuanian, Malay, Maltese, Marathi, Mongolian, Malayalam, Macedonian, Norwegian Bokmal, Nepali, Polish, Pashto, Portuguese, Romanian, Russian, Sinhala, Slovak, Slovenian, Somali, Spanish, Serbian, Sundanese, Swahili, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, and Zulu. 

Text to speech isn’t pronouncing a word correctly. Can this be fixed?

Yes. Text to speech pronunciation can be adjusted with intentional misspelling words (spelling a word as it sounds). For numbers, try writing them out in full, for example, 1998 becomes “nineteen ninety-eight”. 

Can I adjust the volume of a free Ai voiceover?

Yes. The AI text to speech generator lets you adjust the volume using the audio tab on the property panel. Easily move the volume slider to the left to decrease the volume or slide to the right to increase the voiceover volume .  

Turn your text into a voiceover today

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A Proclamation on Lesbian, Gay, Bisexual, Transgender, Queer, and Intersex Pride Month,   2024

During Pride Month, we celebrate the extraordinary courage and contributions of the Lesbian, Gay, Bisexual, Transgender, Queer, and Intersex (LGBTQI+) community.  We reflect on the progress we have made so far in pursuit of equality, justice, and inclusion.  We recommit ourselves to do more to support LGBTQI+ rights at home and around the world. 

For generations, LGBTQI+ Americans have summoned the courage to live authentically and proudly — even when it meant putting their lives and livelihoods at risk.  In 1969 at the Stonewall Inn in New York, brave LGBTQI+ individuals protested the violence and marginalization they faced, boosting a civil rights movement for the liberation of LGBTQI+ people that has transformed our Nation.  Since then, courageous LGBTQI+ Americans continue to inspire and bring hope to all people seeking a life true to who they are.  LGBTQI+ people also continue to enrich every aspect of American life as educators, entertainers, entrepreneurs, athletes, actors, artists, scientists, scholars, diplomats, doctors, service members, veterans, and so much more.

Advancing equality for the LGBTQI+ community is a top priority for my Administration.  I signed the historic Respect for Marriage Act, which protects the marriage of same-sex and interracial couples.  As Commander in Chief, I was proud to have ended the ban on transgender Americans serving in the United States military.  I signed historic Executive Orders strengthening civil rights protections for housing, employment, health care, education, and the justice system.  We are also combating the dangerous and cruel practice of so-called “conversion therapy” and implementing a national strategy to end the HIV epidemic in this country.  We ended the disgraceful practice of banning gay and bisexual men from donating blood.  We are doing this work here at home and around the globe, where LGBTQI+ community members are fighting for recognition of their fundamental human rights and seeking to live full lives, free from hate-fueled violence and discrimination.

But for all the progress, we know real challenges persist.  Last year, as we celebrated Pride Month on the South Lawn of the White House, I had the honor of meeting survivors of the Club Q and Pulse shootings, which tragically took the lives of LGBTQI+ Americans.  Although my Administration passed the most significant gun law in nearly 30 years, the Congress must do its part and ban assault weapons.  At the same time, families across the country face excruciating decisions to relocate to a different State to protect their children from dangerous and hateful anti-LGBTQI+ laws, which target transgender children, threaten families, and criminalize doctors and nurses.  These bills and laws attack our most basic values and freedoms as Americans:  the right to be yourself, the right to make your own medical decisions, and the right to raise your own children.  Some things should never be put at risk:  your life, your safety, and your dignity.

To the entire LGBTQI+ community — and especially transgender children — please know that your President and my entire Administration have your back.  We see you for who you are:  made in the image of God and deserving of dignity, respect, and support.  That is why I have taken historic action to protect the LGBTQI+ community.  We are ensuring that the LGBTQI+ community is protected against discrimination when accessing health care, and the Department of Health and Human Services, Department of Homeland Security, and Department of Justice launched a safety partnership to provide critical training and support to the community, including resources to help report hate crimes and better protect festivals, marches, community centers, businesses, and health care providers serving the community.  The Department of Education and the Department of Justice are also addressing whether book bans may violate Federal civil rights laws when they target LGBTQI+ students or students of color and create hostile classroom environments.  Additionally, we are providing specialized services through the nationwide crisis hotline for LGBTQI+ youth who feel isolated and overwhelmed — anyone who needs help can call 988 and then press 3 to be connected to a professional counselor.  We are committing more resources for mental health programs that help families support and affirm their kids and are starting a new Federal initiative to address LGBTQI+ homelessness.  We finalized new regulations requiring States to protect LGBTQI+ kids in foster care.

America is the only Nation in the world founded on an idea:  We are all created equal and deserve to be treated equally throughout our lives.  We have never fully lived up to that idea, but we have never fully walked away from it either.  This month, we recommit to realizing the promise of America for all Americans, to celebrating courageous LGBTQI+ people, and to taking pride in the example they set for our Nation and the world.  

NOW, THEREFORE, I, JOSEPH R. BIDEN JR., President of the United States of America, by virtue of the authority vested in me by the Constitution and the laws of the United States, do hereby proclaim June 2024 as Lesbian, Gay, Bisexual, Transgender, Queer, and Intersex Pride Month.  I call upon the people of the United States to recognize the achievements of the LGBTQI+ community, to celebrate the great diversity of the American people, and to wave their flags of pride high.

     IN WITNESS WHEREOF, I have hereunto set my hand this thirty-first day of May, in the year of our Lord two thousand twenty-four, and of the Independence of the United States of America the two hundred and forty-eighth.

                              JOSEPH R. BIDEN JR.

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