speech text language

SpeechTexter is a free multilingual speech-to-text application aimed at assisting you with transcription of notes, documents, books, reports or blog posts by using your voice. This app also features a customizable voice commands list, allowing users to add punctuation marks, frequently used phrases, and some app actions (undo, redo, make a new paragraph).

SpeechTexter is used daily by students, teachers, writers, bloggers around the world.

It will assist you in minimizing your writing efforts significantly.

Voice-to-text software is exceptionally valuable for people who have difficulty using their hands due to trauma, people with dyslexia or disabilities that limit the use of conventional input devices. Speech to text technology can also be used to improve accessibility for those with hearing impairments, as it can convert speech into text.

It can also be used as a tool for learning a proper pronunciation of words in the foreign language, in addition to helping a person develop fluency with their speaking skills.

Accuracy levels higher than 90% should be expected. It varies depending on the language and the speaker.

No download, installation or registration is required. Just click the microphone button and start dictating.

Speech to text technology is quickly becoming an essential tool for those looking to save time and increase their productivity.

Powerful real-time continuous speech recognition

Creation of text notes, emails, blog posts, reports and more.

Custom voice commands

More than 70 languages supported

SpeechTexter is using Google Speech recognition to convert the speech into text in real-time. This technology is supported by Chrome browser (for desktop) and some browsers on Android OS. Other browsers have not implemented speech recognition yet.

Note: iPhones and iPads are not supported

List of supported languages:

Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Bengali, Bosnian, Bulgarian, Burmese, Catalan, Chinese (Mandarin, Cantonese), Croatian, Czech, Danish, Dutch, English, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Kinyarwanda, Korean, Lao, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian Bokmål, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Sinhala, Slovak, Slovenian, Southern Sotho, Spanish, Sundanese, Swahili, Swati, Swedish, Tamil, Telugu, Thai, Tsonga, Tswana, Turkish, Ukrainian, Urdu, Uzbek, Venda, Vietnamese, Xhosa, Zulu.

Instructions for web app on desktop (Windows, Mac, Linux OS)

Requirements: the latest version of the Google Chrome [↗] browser (other browsers are not supported).

1. Connect a high-quality microphone to your computer.

2. Make sure your microphone is set as the default recording device on your browser.

To go directly to microphone's settings paste the line below into Chrome's URL bar.

chrome://settings/content/microphone

To capture speech from video/audio content on the web or from a file stored on your device, select 'Stereo Mix' as the default audio input.

3. Select the language you would like to speak (Click the button on the top right corner).

4. Click the "microphone" button. Chrome browser will request your permission to access your microphone. Choose "allow".

5. You can start dictating!

Instructions for the web app on a mobile and for the android app

Requirements: - Google app [↗] installed on your Android device. - Any of the supported browsers if you choose to use the web app.

Supported android browsers (not a full list): Chrome browser (recommended), Edge, Opera, Brave, Vivaldi.

1. Tap the button with the language name (on a web app) or language code (on android app) on the top right corner to select your language.

2. Tap the microphone button. The SpeechTexter app will ask for permission to record audio. Choose 'allow' to enable microphone access.

3. You can start dictating!

Common problems on a desktop (Windows, Mac, Linux OS)

Error: 'speechtexter cannot access your microphone'..

Please give permission to access your microphone.

Click on the "padlock" icon next to the URL bar, find the "microphone" option, and choose "allow".

Error: 'No speech was detected. Please try again'.

If you get this error while you are speaking, make sure your microphone is set as the default recording device on your browser [see step 2].

If you're using a headset, make sure the mute switch on the cord is off.

Error: 'Network error'

The internet connection is poor. Please try again later.

The result won't transfer to the "editor".

The result confidence is not high enough or there is a background noise. An accumulation of long text in the buffer can also make the engine stop responding, please make some pauses in the speech.

The results are wrong.

Please speak loudly and clearly. Speaking clearly and consistently will help the software accurately recognize your words.

Reduce background noise. Background noise from fans, air conditioners, refrigerators, etc. can drop the accuracy significantly. Try to reduce background noise as much as possible.

Speak directly into the microphone. Speaking directly into the microphone enhances the accuracy of the software. Avoid speaking too far away from the microphone.

Speak in complete sentences. Speaking in complete sentences will help the software better recognize the context of your words.

Can I upload an audio file and get the transcription?

No, this feature is not available.

How do I transcribe an audio (video) file on my PC or from the web?

Playback your file in any player and hit the 'mic' button on the SpeechTexter website to start capturing the speech. For better results select "Stereo Mix" as the default recording device on your browser, if you are accessing SpeechTexter and the file from the same device.

I don't see the "Stereo mix" option (Windows OS)

"Stereo Mix" might be hidden or it's not supported by your system. If you are a Windows user go to 'Control panel' → Hardware and Sound → Sound → 'Recording' tab. Right-click on a blank area in the pane and make sure both "View Disabled Devices" and "View Disconnected Devices" options are checked. If "Stereo Mix" appears, you can enable it by right clicking on it and choosing 'enable'. If "Stereo Mix" hasn't appeared, it means it's not supported by your system. You can try using a third-party program such as "Virtual Audio Cable" or "VB-Audio Virtual Cable" to create a virtual audio device that includes "Stereo Mix" functionality.

How to use the voice commands list?

The voice commands list allows you to insert the punctuation, some text, or run some preset functions using only your voice. On the first column you enter your voice command. On the second column you enter a punctuation mark or a function. Voice commands are case-sensitive. Available functions: #newparagraph (add a new paragraph), #undo (undo the last change), #redo (redo the last change)

To use the function above make a pause in your speech until all previous dictated speech appears in your note, then say "insert a new paragraph" and wait for the command execution.

Found a mistake in the voice commands list or want to suggest an update? Follow the steps below:

• Navigate to the voice commands list [↑] on this website.
• Click on the edit button to update or add new punctuation marks you think other users might find useful in your language.
• Click on the "Export" button located above the voice commands list to save your list in JSON format to your device.

Next, send us your file as an attachment via email. You can find the email address at the bottom of the page. Feel free to include a brief description of the mistake or the updates you're suggesting in the email body.

Your contribution to the improvement of the services is appreciated.

Can I prevent my custom voice commands from disappearing after closing the browser?

SpeechTexter by default saves your data inside your browser's cache. If your browsers clears the cache your data will be deleted. However, you can export your custom voice commands to your device and import them when you need them by clicking the corresponding buttons above the list. SpeechTexter is using JSON format to store your voice commands. You can create a .txt file in this format on your device and then import it into SpeechTexter. An example of JSON format is shown below:

{ "period": ".", "full stop": ".", "question mark": "?", "new paragraph": "#newparagraph" }

I lost my dictated work after closing the browser.

SpeechTexter doesn't store any text that you dictate. Please use the "autosave" option or click the "download" button (recommended). The "autosave" option will try to store your work inside your browser's cache, where it will remain until you switch the "text autosave" option off, clear the cache manually, or if your browser clears the cache on exit.

Common problems on the Android app

I get the message: 'speech recognition is not available'..

'Google app' from Play store is required for SpeechTexter to work. download [↗]

Where does SpeechTexter store the saved files?

Version 1.5 and above stores the files in the internal memory.

Version 1.4.9 and below stores the files inside the "SpeechTexter" folder at the root directory of your device.

After updating the app from version 1.x.x to version 2.x.x my files have disappeared

As a result of recent updates, the Android operating system has implemented restrictions that prevent users from accessing folders within the Android root directory, including SpeechTexter's folder. However, your old files can still be imported manually by selecting the "import" button within the Speechtexter application.

Common problems on the mobile web app

Tap on the "padlock" icon next to the URL bar, find the "microphone" option and choose "allow".

• TERMS OF USE
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• Play Store [↗]

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Speech to Text - Voice Typing & Transcription

Take notes with your voice for free, or automatically transcribe audio & video recordings. amazingly accurate, secure & blazing fast..

~ Proudly serving millions of users since 2015 ~

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Dictate Notes

Start taking notes, on our online voice-enabled notepad right away, for free. Learn more.

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Automatically transcribe (& optionally translate) recordings, audio and video files, YouTubes and more, in no time. Learn more.

Speechnotes is a reliable and secure web-based speech-to-text tool that enables you to quickly and accurately transcribe & translate your audio and video recordings, as well as dictate your notes instead of typing, saving you time and effort. With features like voice commands for punctuation and formatting, automatic capitalization, and easy import/export options, Speechnotes provides an efficient and user-friendly dictation and transcription experience. Proudly serving millions of users since 2015, Speechnotes is the go-to tool for anyone who needs fast, accurate & private transcription. Our Portfolio of Complementary Speech-To-Text Tools Includes:

Voice typing - Chrome extension

Dictate instead of typing on any form & text-box across the web. Including on Gmail, and more.

Transcription API & webhooks

Speechnotes' API enables you to send us files via standard POST requests, and get the transcription results sent directly to your server.

Zapier integration

Combine the power of automatic transcriptions with Zapier's automatic processes. Serverless & codeless automation! Connect with your CRM, phone calls, Docs, email & more.

Android Speechnotes app

Speechnotes' notepad for Android, for notes taking on your mobile, battle tested with more than 5Million downloads. Rated 4.3+ ⭐

iOS TextHear app

TextHear for iOS, works great on iPhones, iPads & Macs. Designed specifically to help people with hearing impairment participate in conversations. Please note, this is a sister app - so it has its own pricing plan.

Audio & video converting tools

Tools developed for fast - batch conversions of audio files from one type to another and extracting audio only from videos for minimizing uploads.

Our Sister Apps for Text-To-Speech & Live Captioning

Complementary to Speechnotes

Reads out loud texts, files & web pages

Listen on the go to any written content, from custom texts to websites & e-books, for free.

Speechlogger

Live Captioning & Translation

Live captions & simultaneous translation for conferences, online meetings, webinars & more.

Need Human Transcription? We Can Offer a 10% Discount Coupon

We do not provide human transcription services ourselves, but, we partnered with a UK company that does. Learn more on human transcription and the 10% discount .

Dictation Notepad

Start taking notes with your voice for free

Speech to Text online notepad. Professional, accurate & free speech recognizing text editor. Distraction-free, fast, easy to use web app for dictation & typing.

Speechnotes is a powerful speech-enabled online notepad, designed to empower your ideas by implementing a clean & efficient design, so you can focus on your thoughts. We strive to provide the best online dictation tool by engaging cutting-edge speech-recognition technology for the most accurate results technology can achieve today, together with incorporating built-in tools (automatic or manual) to increase users' efficiency, productivity and comfort. Works entirely online in your Chrome browser. No download, no install and even no registration needed, so you can start working right away.

Speechnotes is especially designed to provide you a distraction-free environment. Every note, starts with a new clear white paper, so to stimulate your mind with a clean fresh start. All other elements but the text itself are out of sight by fading out, so you can concentrate on the most important part - your own creativity. In addition to that, speaking instead of typing, enables you to think and speak it out fluently, uninterrupted, which again encourages creative, clear thinking. Fonts and colors all over the app were designed to be sharp and have excellent legibility characteristics.

Example use cases

• Voice typing
• Writing notes, thoughts
• Medical forms - dictate
• Transcribers (listen and dictate)

Transcription Service

Start transcribing

Fast turnaround - results within minutes. Includes timestamps, auto punctuation and subtitles at unbeatable price. Protects your privacy: no human in the loop, and (unlike many other vendors) we do NOT keep your audio. Pay per use, no recurring payments. Upload your files or transcribe directly from Google Drive, YouTube or any other online source. Simple. No download or install. Just send us the file and get the results in minutes.

• Transcribe interviews
• Captions for Youtubes & movies
• Auto-transcribe phone calls or voice messages
• Students - transcribe lectures
• Podcasters - enlarge your audience by turning your podcasts into textual content
• Text-index entire audio archives

Key Advantages

Speechnotes is powered by the leading most accurate speech recognition AI engines by Google & Microsoft. We always check - and make sure we still use the best. Accuracy in English is very good and can easily reach 95% accuracy for good quality dictation or recording.

Lightweight & fast

Both Speechnotes dictation & transcription are lightweight-online no install, work out of the box anywhere you are. Dictation works in real time. Transcription will get you results in a matter of minutes.

Super Private & Secure!

Super private - no human handles, sees or listens to your recordings! In addition, we take great measures to protect your privacy. For example, for transcribing your recordings - we pay Google's speech to text engines extra - just so they do not keep your audio for their own research purposes.

Health advantages

Typing may result in different types of Computer Related Repetitive Strain Injuries (RSI). Voice typing is one of the main recommended ways to minimize these risks, as it enables you to sit back comfortably, freeing your arms, hands, shoulders and back altogether.

Saves you time

Need to transcribe a recording? If it's an hour long, transcribing it yourself will take you about 6! hours of work. If you send it to a transcriber - you will get it back in days! Upload it to Speechnotes - it will take you less than a minute, and you will get the results in about 20 minutes to your email.

Saves you money

Speechnotes dictation notepad is completely free - with ads - or a small fee to get it ad-free. Speechnotes transcription is only $0.1/minute, which is X10 times cheaper than a human transcriber! We offer the best deal on the market - whether it's the free dictation notepad ot the pay-as-you-go transcription service.

Dictation - Free

• Online dictation notepad
• Voice typing Chrome extension

Dictation - Premium

• Premium online dictation notepad
• Premium voice typing Chrome extension
• Support from the development team

Transcription

$0.1 /minute.

• Pay as you go - no subscription
• Audio & video recordings
• Speaker diarization in English
• Generate captions .srt files
• REST API, webhooks & Zapier integration

Compare plans

Privacy Policy

We at Speechnotes, Speechlogger, TextHear, Speechkeys value your privacy, and that's why we do not store anything you say or type or in fact any other data about you - unless it is solely needed for the purpose of your operation. We don't share it with 3rd parties, other than Google / Microsoft for the speech-to-text engine.

Privacy - how are the recordings and results handled?

- transcription service.

Our transcription service is probably the most private and secure transcription service available.

• HIPAA compliant.
• No human in the loop. No passing your recording between PCs, emails, employees, etc.
• Secure encrypted communications (https) with and between our servers.
• Recordings are automatically deleted from our servers as soon as the transcription is done.
• Our contract with Google / Microsoft (our speech engines providers) prohibits them from keeping any audio or results.
• Transcription results are securely kept on our secure database. Only you have access to them - only if you sign in (or provide your secret credentials through the API)
• You may choose to delete the transcription results - once you do - no copy remains on our servers.

- Dictation notepad & extension

For dictation, the recording & recognition - is delegated to and done by the browser (Chrome / Edge) or operating system (Android). So, we never even have access to the recorded audio, and Edge's / Chrome's / Android's (depending the one you use) privacy policy apply here.

The results of the dictation are saved locally on your machine - via the browser's / app's local storage. It never gets to our servers. So, as long as your device is private - your notes are private.

Payments method privacy

The whole payments process is delegated to PayPal / Stripe / Google Pay / Play Store / App Store and secured by these providers. We never receive any of your credit card information.

More generic notes regarding our site, cookies, analytics, ads, etc.

• We may use Google Analytics on our site - which is a generic tool to track usage statistics.
• We use cookies - which means we save data on your browser to send to our servers when needed. This is used for instance to sign you in, and then keep you signed in.
• For the dictation tool - we use your browser's local storage to store your notes, so you can access them later.
• Non premium dictation tool serves ads by Google. Users may opt out of personalized advertising by visiting Ads Settings . Alternatively, users can opt out of a third-party vendor's use of cookies for personalized advertising by visiting https://youradchoices.com/
• In case you would like to upload files to Google Drive directly from Speechnotes - we'll ask for your permission to do so. We will use that permission for that purpose only - syncing your speech-notes to your Google Drive, per your request.

September 21, 2022

Introducing Whisper

We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.

Illustration: Ruby Chen

Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English. We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing.

The Whisper architecture is a simple end-to-end approach, implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption, intermixed with special tokens that direct the single model to perform tasks such as language identification, phrase-level timestamps, multilingual speech transcription, and to-English speech translation.

Other existing approaches frequently use smaller, more closely paired audio-text training datasets, 1 2 , 3 or use broad but unsupervised audio pretraining. 4 , 5 , 6  Because Whisper was trained on a large and diverse dataset and was not fine-tuned to any specific one, it does not beat models that specialize in LibriSpeech performance, a famously competitive benchmark in speech recognition. However, when we measure Whisper’s zero-shot performance across many diverse datasets we find it is much more robust and makes 50% fewer errors than those models.

About a third of Whisper’s audio dataset is non-English, and it is alternately given the task of transcribing in the original language or translating to English. We find this approach is particularly effective at learning speech to text translation and outperforms the supervised SOTA on CoVoST2 to English translation zero-shot.

We hope Whisper’s high accuracy and ease of use will allow developers to add voice interfaces to a much wider set of applications. Check out the  paper (opens in a new window) ,  model card (opens in a new window) , and  code (opens in a new window)  to learn more details and to try out Whisper.

• Publication
• Open source
• Speech recognition
• Transformers

Chan, W., Park, D., Lee, C., Zhang, Y., Le, Q., and Norouzi, M. SpeechStew: Simply mix all available speech recogni- tion data to train one large neural network.  arXiv preprint arXiv:2104.02133, 2021 (opens in a new window) .

Galvez, D., Diamos, G., Torres, J. M. C., Achorn, K., Gopi, A., Kanter, D., Lam, M., Mazumder, M., and Reddi, V. J. The people’s speech: A large-scale diverse english speech recognition dataset for commercial usage.  arXiv preprint arXiv:2111.09344, 2021 (opens in a new window) .

Chen, G., Chai, S., Wang, G., Du, J., Zhang, W.-Q., Weng, C., Su, D., Povey, D., Trmal, J., Zhang, J., et al. Gigaspeech: An evolving, multi-domain asr corpus with 10,000 hours of transcribed audio.  arXiv preprint arXiv:2106.06909, 2021 (opens in a new window) .

Baevski, A., Zhou, H., Mohamed, A., and Auli, M. wav2vec 2.0: A framework for self-supervised learning of speech representations.  arXiv preprint arXiv:2006.11477, 2020 (opens in a new window) .

Baevski, A., Hsu, W.N., Conneau, A., and Auli, M. Unsu pervised speech recognition. Advances in Neural Information Processing Systems, 34:27826–27839, 2021.

Zhang, Y., Park, D. S., Han, W., Qin, J., Gulati, A., Shor, J., Jansen, A., Xu, Y., Huang, Y., Wang, S., et al. BigSSL: Exploring the frontier of large-scale semi-supervised learning for automatic speech recognition.  arXiv preprint arXiv:2109.13226, 2021 (opens in a new window) .

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We built our app for many use-cases

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Emily Williams

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Listen to web pages

TTSReader can be used to read out loud webpages in two different ways. 1. Using the regular player - paste the URL and click play. The website's content will be imported into the player. (2) Using our Chrome extension to listen to pages without leaving the page . Listening to web pages with TTSReader can provide a more accessible, convenient, and efficient way of consuming online content.

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TTSReader enables exporting the synthesized speech with a single click. This is available currently only on Windows and requires TTSReader’s premium . Adhering to the commercial terms some of the voices may be used commercially for publishing, such as narrating videos.

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Ttsreader is a free text to speech reader that supports all modern browsers, including chrome, firefox and safari..

Includes multiple languages and accents. If on Chrome - you will get access to Google's voices as well. Super easy to use - no download, no login required. Here are some more features

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Vs. Recorded Podcasts

In many aspects, synthesized speech has advantages over recorded podcasts. Here are some: First of all - you have unlimited - free - content. That includes high-quality articles and books, that are not available on podcasts. Second - it’s free. Third - it uses almost no data - so it’s available offline too, and you save money. If you like listening on the go, as while driving or walking - get our free Android Text Reader App .

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TTSReader enables exporting the synthesized speech to mp3 audio files. This is available currently only on Windows, and requires ttsreader’s premium .

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Enable language recognition in Speech-to-Text

This page describes how to enable language recognition for audio transcription requests sent to Speech-to-Text.

In some situations, you don't know for certain what language your audio recordings contain. For example, if you publish your service, app, or product in a country with multiple official languages, you can potentially receive audio input from users in a variety of languages. This can make specifying a single language code for transcription requests significantly more difficult.

Multiple language recognition

Speech-to-Text offers a way for you to specify a set of alternative languages that your audio data might contain. When you send an audio transcription request to Speech-to-Text, you can provide a list of additional languages that the audio data might include. If you include a list of languages in your request, Speech-to-Text attempts to transcribe the audio based upon the language that best fits the sample from the alternates you provide. Speech-to-Text then labels the transcription results with the predicted language code.

This feature is ideal for apps that need to transcribe short statements like voice commands or search. You can list up to three alternative languages from among those that Speech-to-Text supports in addition to your primary language (for four languages total).

Even though you can specify alternative languages for your speech transcription request, you must still provide a primary language code in the languageCode field. Also, you should constrain the number of languages you request to a bare minimum. The fewer alternative language codes that you request helps Speech-to-Text more successfully select the correct one. Specifying just a single language produces the best results.

Enable language recognition in audio transcription requests

To specify alternative languages in your audio transcription, you must set the alternativeLanguageCodes field to a list of language codes in the RecognitionConfig parameters for the request. Speech-to-Text supports alternative language codes for all speech recognition methods: speech:recognize , speech:longrunningrecognize , and Streaming .

Use a local file

Refer to the speech:recognize API endpoint for complete details.

To perform synchronous speech recognition, make a POST request and provide the appropriate request body. The following shows an example of a POST request using curl . The example uses the Google Cloud CLI to generate an access token. For instructions on installing the gcloud CLI, see the quickstart .

The following example shows how to request transcription of an audio file that may include speech in English, French, or German.

If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format, saved to a file named multi-language.txt .

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries . For more information, see the Speech-to-Text Java API reference documentation .

To authenticate to Speech-to-Text, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries . For more information, see the Speech-to-Text Node.js API reference documentation .

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries . For more information, see the Speech-to-Text Python API reference documentation .

Use a remote file

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.

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Title: cross-dialect text-to-speech in pitch-accent language incorporating multi-dialect phoneme-level bert.

Abstract: We explore cross-dialect text-to-speech (CD-TTS), a task to synthesize learned speakers' voices in non-native dialects, especially in pitch-accent languages. CD-TTS is important for developing voice agents that naturally communicate with people across regions. We present a novel TTS model comprising three sub-modules to perform competitively at this task. We first train a backbone TTS model to synthesize dialect speech from a text conditioned on phoneme-level accent latent variables (ALVs) extracted from speech by a reference encoder. Then, we train an ALV predictor to predict ALVs tailored to a target dialect from input text leveraging our novel multi-dialect phoneme-level BERT. We conduct multi-dialect TTS experiments and evaluate the effectiveness of our model by comparing it with a baseline derived from conventional dialect TTS methods. The results show that our model improves the dialectal naturalness of synthetic speech in CD-TTS.

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12 Sep 2024  ·  Zhiyuan Tang , Dong Wang , Shen Huang , Shidong Shang · Edit social preview

Large Language Models (LLMs) have demonstrated substantial potential for error correction in Automatic Speech Recognition (ASR). However, most research focuses on utterances from short-duration speech recordings, which are the predominant form of speech data for supervised ASR training. This paper investigates the effectiveness of LLMs for error correction in full-text generated by ASR systems from longer speech recordings, such as transcripts from podcasts, news broadcasts, and meetings. First, we develop a Chinese dataset for full-text error correction, named ChFT, utilizing a pipeline that involves text-to-speech synthesis, ASR, and error-correction pair extractor. This dataset enables us to correct errors across contexts, including both full-text and segment, and to address a broader range of error types, such as punctuation restoration and inverse text normalization, thus making the correction process comprehensive. Second, we fine-tune a pre-trained LLM on the constructed dataset using a diverse set of prompts and target formats, and evaluate its performance on full-text error correction. Specifically, we design prompts based on full-text and segment, considering various output formats, such as directly corrected text and JSON-based error-correction pairs. Through various test settings, including homogeneous, up-to-date, and hard test sets, we find that the fine-tuned LLMs perform well in the full-text setting with different prompts, each presenting its own strengths and weaknesses. This establishes a promising baseline for further research. The dataset is available on the website.

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The Speech-to-Text Analysis feature in iMotions Lab, powered by AssemblyAI , transforms audio recordings into transcribed text, offering valuable insights into spoken content. This feature is designed to analyze the semantic content of speech, focusing on the meaning and sentiment behind the words rather than technical aspects like pitch or tone.

iMotions’ integration with AssemblyAI provides seamless transcription in a wide range of languages, including English (US, British, Australian, and Global), Dutch, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish. For even more versatility, the AssemblyAI nano model supports over 70 languages, including Arabic, Chinese, Russian, and many others.

Beyond transcription, advanced features such as speaker identification and sentiment analysis are available for English-language recordings. This makes it an ideal tool for researchers and organizations seeking to assess verbal communication in a variety of contexts, from customer feedback to academic studies.

To begin using this feature, users need an AssemblyAI account , and processing audio through iMotions is easy and fully integrated within the iMotions platform. Whether analyzing single sessions or large studies, results are stored as annotations, streamlining your analysis workflow.

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How to recognize and translate speech

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Reference documentation | Package (NuGet) | Additional samples on GitHub

In this how-to guide, you learn how to recognize human speech and translate it to another language.

See the speech translation overview for more information about:

• Translating speech to text
• Translating speech to multiple target languages
• Performing direct speech to speech translation

Sensitive data and environment variables

The example source code in this article depends on environment variables for storing sensitive data, such as the Speech resource's key and region. The Program class contains two static readonly string values that are assigned from the host machine's environment variables: SPEECH__SUBSCRIPTION__KEY and SPEECH__SERVICE__REGION . Both of these fields are at the class scope, so they're accessible within method bodies of the class:

For more information on environment variables, see Environment variables and application configuration .

If you use an API key, store it securely somewhere else, such as in Azure Key Vault . Don't include the API key directly in your code, and never post it publicly.

For more information about AI services security, see Authenticate requests to Azure AI services .

Create a speech translation configuration

To call the Speech service by using the Speech SDK, you need to create a SpeechTranslationConfig instance. This class includes information about your subscription, like your key and associated region, endpoint, host, or authorization token.

Regardless of whether you're performing speech recognition, speech synthesis, translation, or intent recognition, you'll always create a configuration.

You can initialize SpeechTranslationConfig in a few ways:

• With a subscription: pass in a key and the associated region.
• With an endpoint: pass in a Speech service endpoint. A key or authorization token is optional.
• With a host: pass in a host address. A key or authorization token is optional.
• With an authorization token: pass in an authorization token and the associated region.

Let's look at how you create a SpeechTranslationConfig instance by using a key and region. Get the Speech resource key and region in the Azure portal .

Change the source language

One common task of speech translation is specifying the input (or source) language. The following example shows how you would change the input language to Italian. In your code, interact with the SpeechTranslationConfig instance by assigning it to the SpeechRecognitionLanguage property:

The SpeechRecognitionLanguage property expects a language-locale format string. Refer to the list of supported speech translation locales .

Add a translation language

Another common task of speech translation is to specify target translation languages. At least one is required, but multiples are supported. The following code snippet sets both French and German as translation language targets:

With every call to AddTargetLanguage , a new target translation language is specified. In other words, when speech is recognized from the source language, each target translation is available as part of the resulting translation operation.

Initialize a translation recognizer

After you created a SpeechTranslationConfig instance, the next step is to initialize TranslationRecognizer . When you initialize TranslationRecognizer , you need to pass it your speechTranslationConfig instance. The configuration object provides the credentials that the Speech service requires to validate your request.

If you're recognizing speech by using your device's default microphone, here's what the TranslationRecognizer instance should look like:

If you want to specify the audio input device, then you need to create an AudioConfig class instance and provide the audioConfig parameter when initializing TranslationRecognizer .

Learn how to get the device ID for your audio input device .

First, reference the AudioConfig object as follows:

If you want to provide an audio file instead of using a microphone, you still need to provide an audioConfig parameter. However, when you create an AudioConfig class instance, instead of calling FromDefaultMicrophoneInput , you call FromWavFileInput and pass the filename parameter:

Translate speech

To translate speech, the Speech SDK relies on a microphone or an audio file input. Speech recognition occurs before speech translation. After all objects are initialized, call the recognize-once function and get the result:

For more information about speech to text, see the basics of speech recognition .

Event based translation

The TranslationRecognizer object exposes a Recognizing event. The event fires several times and provides a mechanism to retrieve the intermediate translation results.

Intermediate translation results aren't available when you use multi-lingual speech translation .

The following example prints the intermediate translation results to the console:

Synthesize translations

After a successful speech recognition and translation, the result contains all the translations in a dictionary. The Translations dictionary key is the target translation language, and the value is the translated text. Recognized speech can be translated and then synthesized in a different language (speech-to-speech).

Event-based synthesis

The TranslationRecognizer object exposes a Synthesizing event. The event fires several times and provides a mechanism to retrieve the synthesized audio from the translation recognition result. If you're translating to multiple languages, see Manual synthesis .

Specify the synthesis voice by assigning a VoiceName instance, and provide an event handler for the Synthesizing event to get the audio. The following example saves the translated audio as a .wav file.

The event-based synthesis works only with a single translation. Do not add multiple target translation languages. Additionally, the VoiceName value should be the same language as the target translation language. For example, "de" could map to "de-DE-Hedda" .

Manual synthesis

You can use the Translations dictionary to synthesize audio from the translation text. Iterate through each translation and synthesize it. When you're creating a SpeechSynthesizer instance, the SpeechConfig object needs to have its SpeechSynthesisVoiceName property set to the desired voice.

The following example translates to five languages. Each translation is then synthesized to an audio file in the corresponding neural language.

For more information about speech synthesis, see the basics of speech synthesis .

Multi-lingual translation with language identification

In many scenarios, you might not know which input languages to specify. Using language identification you can detect up to 10 possible input languages and automatically translate to your target languages.

The following example anticipates that en-US or zh-CN should be detected because they're defined in AutoDetectSourceLanguageConfig . Then, the speech is translated to de and fr as specified in the calls to AddTargetLanguage() .

For a complete code sample, see language identification .

Multi-lingual speech translation without source language candidates

Multi-lingual speech translation implements a new level of speech translation technology that unlocks various capabilities, including having no specified input language, and handling language switches within the same session. These features enable a new level of speech translation powers that can be implemented into your products.

Currently when you use Language ID with speech translation, you must create the SpeechTranslationConfig object from the v2 endpoint. Replace the string "YourServiceRegion" with your Speech resource region (such as "westus"). Replace "YourSubscriptionKey" with your Speech resource key.

Specify the translation target languages. Replace with languages of your choice. You can add more lines.

A key differentiator with multi-lingual speech translation is that you do not need to specify the source language. This is because the service will automatically detect the source language. Create the AutoDetectSourceLanguageConfig object with the fromOpenRange method to let the service know that you want to use multi-lingual speech translation with no specified source language.

For a complete code sample with the Speech SDK, see speech translation samples on GitHub .

Using custom translation in speech translation

The custom translation feature in speech translation seamlessly integrates with the Azure Custom Translation service, allowing you to achieve more accurate and tailored translations. As the integration directly harnesses the capabilities of the Azure custom translation service, you need to use a multi-service resource to ensure the correct functioning of the complete set of features. For detailed instructions, please consult the guide on Create a multi-service resource for Azure AI services .

Additionally, for offline training of a custom translator and obtaining a "Category ID," please refer to the step-by-step script provided in the Quickstart: Build, deploy, and use a custom model - Custom Translator .

The example source code in this article depends on environment variables for storing sensitive data, such as the Speech resource's key and region. The C++ code file contains two string values that are assigned from the host machine's environment variables: SPEECH__SUBSCRIPTION__KEY and SPEECH__SERVICE__REGION . Both of these fields are at the class scope, so they're accessible within method bodies of the class:

One common task of speech translation is specifying the input (or source) language. The following example shows how you would change the input language to Italian. In your code, interact with the SpeechTranslationConfig instance by calling the SetSpeechRecognitionLanguage method.

After you created a SpeechTranslationConfig instance, the next step is to initialize TranslationRecognizer . When you initialize TranslationRecognizer , you need to pass it your translationConfig instance. The configuration object provides the credentials that the Speech service requires to validate your request.

If you're recognizing speech by using your device's default microphone, here's what TranslationRecognizer should look like:

Specify the synthesis voice by assigning a SetVoiceName instance, and provide an event handler for the Synthesizing event to get the audio. The following example saves the translated audio as a .wav file.

The event-based synthesis works only with a single translation. Do not add multiple target translation languages. Additionally, the SetVoiceName value should be the same language as the target translation language. For example, "de" could map to "de-DE-Hedda" .

You can use the Translations dictionary to synthesize audio from the translation text. Iterate through each translation and synthesize it. When you're creating a SpeechSynthesizer instance, the SpeechConfig object needs to have its SetSpeechSynthesisVoiceName property set to the desired voice.

Multilingual translation with language identification

The following example anticipates that en-US or zh-CN should be detected because they're defined in AutoDetectSourceLanguageConfig . Then, the speech will be translated to de and fr as specified in the calls to AddTargetLanguage() .

Reference documentation | Package (Go) | Additional samples on GitHub

The Speech SDK for Go does not support speech translation. Please select another programming language or the Go reference and samples linked from the beginning of this article.

Reference documentation | Additional samples on GitHub

The example source code in this article depends on environment variables for storing sensitive data, such as the Speech resource's key and region. The Java code file contains two static final String values that are assigned from the host machine's environment variables: SPEECH__SUBSCRIPTION__KEY and SPEECH__SERVICE__REGION . Both of these fields are at the class scope, so they're accessible within method bodies of the class:

You can initialize a SpeechTranslationConfig instance in a few ways:

One common task of speech translation is specifying the input (or source) language. The following example shows how you would change the input language to Italian. In your code, interact with the SpeechTranslationConfig instance by calling the setSpeechRecognitionLanguage method:

The setSpeechRecognitionLanguage function expects a language-locale format string. Refer to the list of supported speech translation locales .

With every call to addTargetLanguage , a new target translation language is specified. In other words, when speech is recognized from the source language, each target translation is available as part of the resulting translation operation.

If you want to provide an audio file instead of using a microphone, you still need to provide an audioConfig parameter. However, when you create an AudioConfig class instance, instead of calling fromDefaultMicrophoneInput , you call fromWavFileInput and pass the filename parameter:

After a successful speech recognition and translation, the result contains all the translations in a dictionary. The getTranslations function returns a dictionary with the key as the target translation language and the value as the translated text. Recognized speech can be translated and then synthesized in a different language (speech-to-speech).

The TranslationRecognizer object exposes a synthesizing event. The event fires several times and provides a mechanism to retrieve the synthesized audio from the translation recognition result. If you're translating to multiple languages, see Manual synthesis .

Specify the synthesis voice by assigning a setVoiceName instance, and provide an event handler for the synthesizing event to get the audio. The following example saves the translated audio as a .wav file.

The event-based synthesis works only with a single translation. Do not add multiple target translation languages. Additionally, the setVoiceName value should be the same language as the target translation language. For example, "de" could map to "de-DE-Hedda" .

The getTranslations function returns a dictionary that you can use to synthesize audio from the translation text. Iterate through each translation and synthesize it. When you're creating a SpeechSynthesizer instance, the SpeechConfig object needs to have its setSpeechSynthesisVoiceName property set to the desired voice.

Reference documentation | Package (npm) | Additional samples on GitHub | Library source code

Create a translation configuration

To call the translation service by using the Speech SDK, you need to create a SpeechTranslationConfig instance. This class includes information about your subscription, like your key and associated region, endpoint, host, or authorization token.

Initialize a translator

After you created a SpeechTranslationConfig instance, the next step is to initialize TranslationRecognizer . When you initialize TranslationRecognizer , you need to pass it your speechTranslationConfig instance. The configuration object provides the credentials that the translation service requires to validate your request.

If you're translating speech provided through your device's default microphone, here's what TranslationRecognizer should look like:

Reference the AudioConfig object as follows:

If you want to provide an audio file instead of using a microphone, you still need to provide an audioConfig parameter. However, you can do this only when you're targeting Node.js. When you create an AudioConfig class instance, instead of calling fromDefaultMicrophoneInput , you call fromWavFileOutput and pass the filename parameter:

The TranslationRecognizer class for the Speech SDK for JavaScript exposes methods that you can use for speech translation:

• Single-shot translation (async) : Performs translation in a nonblocking (asynchronous) mode. It translates a single utterance. It determines the end of a single utterance by listening for silence at the end or until a maximum of 15 seconds of audio is processed.
• Continuous translation (async) : Asynchronously initiates a continuous translation operation. The user registers to events and handles various application states. To stop asynchronous continuous translation, call stopContinuousRecognitionAsync .

To learn more about how to choose a speech recognition mode, see Get started with speech to text .

Specify a target language

To translate, you must specify both a source language and at least one target language.

You can choose a source language by using a locale listed in the Speech translation table . Find your options for translated language at the same link.

Your options for target languages differ when you want to view text or you want to hear synthesized translated speech. To translate from English to German, modify the translation configuration object:

Single-shot recognition

Here's an example of asynchronous single-shot translation via recognizeOnceAsync :

You need to write some code to handle the result. This sample evaluates result.reason for a translation to German:

Your code can also handle updates provided while the translation is processing. You can use these updates to provide visual feedback about the translation progress. This JavaScript Node.js example shows these kinds of updates. The following code also displays details produced during the translation process:

Continuous translation

Continuous translation is a bit more involved than single-shot recognition. It requires you to subscribe to the recognizing , recognized , and canceled events to get the recognition results. To stop translation, you must call stopContinuousRecognitionAsync .

Here's an example of how continuous translation is performed on an audio input file. Let's start by defining the input and initializing TranslationRecognizer :

In the following code, you subscribe to the events sent from TranslationRecognizer :

• recognizing : Signal for events that contain intermediate translation results.
• recognized : Signal for events that contain final translation results. These results indicate a successful translation attempt.
• sessionStopped : Signal for events that indicate the end of a translation session (operation).
• canceled : Signal for events that contain canceled translation results. These events indicate a translation attempt that was canceled as a result of a direct cancellation. Alternatively, they indicate a transport or protocol failure.

With everything set up, you can call startContinuousRecognitionAsync :

Choose a source language

A common task for speech translation is specifying the input (or source) language. The following example shows how you would change the input language to Italian. In your code, find your SpeechTranslationConfig instance and add the following line directly below it:

The speechRecognitionLanguage property expects a language-locale format string. Refer to the list of supported speech translation locales .

Choose one or more target languages

The Speech SDK can translate to multiple target languages in parallel. The available target languages are somewhat different from the source language list. You specify target languages by using a language code, rather than a locale.

For a list of language codes for text targets, see the speech translation table on the language support page . You can also find details about translation to synthesized languages there.

The following code adds German as a target language:

Because multiple target language translations are possible, your code must specify the target language when examining the result. The following code gets translation results for German:

Reference documentation | Package (download) | Additional samples on GitHub

The Speech SDK for Objective-C does support speech translation, but we haven't yet included a guide here. Please select another programming language to get started and learn about the concepts, or see the Objective-C reference and samples linked from the beginning of this article.

The Speech SDK for Swift does support speech translation, but we haven't yet included a guide here. Please select another programming language to get started and learn about the concepts, or see the Swift reference and samples linked from the beginning of this article.

Reference documentation | Package (PyPi) | Additional samples on GitHub

The example source code in this article depends on environment variables for storing sensitive data, such as the Speech resource's subscription key and region. The Python code file contains two values that are assigned from the host machine's environment variables: SPEECH__SUBSCRIPTION__KEY and SPEECH__SERVICE__REGION . Both of these variables are at the global scope, so they're accessible within the function definition of the code file:

Let's look at how you can create a SpeechTranslationConfig instance by using a key and region. Get the Speech resource key and region in the Azure portal .

One common task of speech translation is specifying the input (or source) language. The following example shows how you would change the input language to Italian. In your code, interact with the SpeechTranslationConfig instance by assigning it to the speech_recognition_language property.

The speech_recognition_language property expects a language-locale format string. Refer to the list of supported speech translation locales .

With every call to add_target_language , a new target translation language is specified. In other words, when speech is recognized from the source language, each target translation is available as part of the resulting translation operation.

After you created a SpeechTranslationConfig instance, the next step is to initialize TranslationRecognizer . When you initialize TranslationRecognizer , you need to pass it your translation_config instance. The configuration object provides the credentials that the Speech service requires to validate your request.

If you want to specify the audio input device, then you need to create an AudioConfig class instance and provide the audio_config parameter when initializing TranslationRecognizer .

If you want to provide an audio file instead of using a microphone, you still need to provide an audioConfig parameter. However, when you create an AudioConfig class instance, instead of calling with use_default_microphone=True , you call with filename="path-to-file.wav" and provide the filename parameter:

After a successful speech recognition and translation, the result contains all the translations in a dictionary. The translations dictionary key is the target translation language, and the value is the translated text. Recognized speech can be translated and then synthesized in a different language (speech-to-speech).

Specify the synthesis voice by assigning a voice_name instance, and provide an event handler for the Synthesizing event to get the audio. The following example saves the translated audio as a .wav file.

The event-based synthesis works only with a single translation. Do not add multiple target translation languages. Additionally, the voice_name value should be the same language as the target translation language. For example, "de" could map to "de-DE-Hedda" .

You can use the translations dictionary to synthesize audio from the translation text. Iterate through each translation and synthesize it. When you're creating a SpeechSynthesizer instance, the SpeechConfig object needs to have its speech_synthesis_voice_name property set to the desired voice.

Speech to text REST API reference | Speech to text REST API for short audio reference | Additional samples on GitHub

You can use the REST API for speech translation, but we haven't yet included a guide here. Please select another programming language to get started and learn about the concepts.

Prerequisites

• An Azure subscription. You can create one for free .
• Create a Speech resource in the Azure portal.
• Get the Speech resource key and region. After your Speech resource is deployed, select Go to resource to view and manage keys.

Download and install

Follow these steps and see the Speech CLI quickstart for other requirements for your platform.

Run the following .NET CLI command to install the Speech CLI:

Run the following commands to configure your Speech resource key and region. Replace SUBSCRIPTION-KEY with your Speech resource key and replace REGION with your Speech resource region.

Set source and target languages

This command calls the Speech CLI to translate speech from the microphone from Italian to French:

• Try the speech to text quickstart
• Try the speech translation quickstart
• Improve recognition accuracy with custom speech

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