Speech Recognition Using Neural Networks, PHD Thesis (1995)
(PDF) Speech Recognition Techniques: A Review
VIDEO
Sound Capture and Speech Enhancement for Communication and Distant Speech Recognition
Automatic Speech Recognition: An Overview
Thesis Statement
ASR / speech-to-text with Whisper at Stanford Libraries. P Leonard
Textless Speech-to-Speech Translation on Real Data #nlp #SpeechProcessing
Deep and segmental convolutional neural networks for speech recognition
COMMENTS
PDF DEEP NEURAL NETWORKS IN SPEECH RECOGNITION A DISSERTATION ...
This thesis comes from a close collaboration with my advisor, Andrew Ng. Andrew has been an amazing mentor in the process of planning and solving research problems, and constantly encouraged me to work on challenging, impactful problems. Much of the work on speech recognition in this thesis comes from close collab-oration with Dan Jurafsky.
PDF Speech Recognition using Neural Networks
This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art ...
PDF Effective Attention-Based Automatic Speech Recognition
In this thesis, we study attention-based sequence-to-sequence ASR models and address the aforementioned issues. We investigate recurrent neural network (RNN) encoder-decoder models and self-attention encoder-decoder models. For RNN encoder- decoder models, we develop a dynamic subsampling RNN (dsRNN) encoder to shorten the lengths of the input ...
PDF Deep learning approaches to problems in speech recognition
of this approach in a series of diverse case studies in speech recognition, computational chemistry, and natural language processing. Throughout these studies, I extend and modify the neural network models as needed to be more e ective for each task. In the area of speech recognition, I develop a more accurate acoustic model using a deep neural ...
PDF Semi-supervised Training for Automatic Speech Recognition
AUTOMATIC SPEECH RECOGNITION by Vimal Manohar A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of ... In the second part of this thesis, we investigate using lattice-based supervi-sion as numerator graph to incorporate uncertainties in unsupervised data in
PDF Model-based Approaches to Robust Speech Recognition in Diverse Environments
Many speech recognition applications will bene t from distant-talking speech capture. This avoids problems caused by using hand-held or body-worn equipment. However, due to the large speaker-to-microphone distance, both background noise and reverberant noise will signi cantly corrupt speech signals and negatively impact speech recognition ...
PDF Acoustical and Environmental Robustness in Automatic Speech Recognition
robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in different acoustical environments, and when a desk-top microphone (rather than a close-talking microphone) is used for ...
(PDF) speech recognition and application
ABSTRACT. In this thesis, speech recognition systems are developed. The se applications are medium-sized, discrete and individual-. dependent systems. In these sy stems, training and testing ...
PDF Feature‑based robust techniques for speech recognition
remains a challenge. In this thesis, speech feature enhancement and model adaptation for robust speech recognition is studied, and three novel methods to improve performance are introduced. The rst work proposes a modi cation of the spectral subtraction method to reduce the non-stationary characteristics of additive noise in the speech.
PDF Multi-Modal and Deep Learning for Robust Speech Recognition
Automatic speech recognition (ASR) decodes speech signals into text. While ASR can pro-duce accurate word recognition in clean environments, system performance can degrade dramatically when noise and reverberation are present. In this thesis, speech denoising and model adaptation for robust speech recognition were studied, and four novel meth-
PDF Exploring Automatic Speech Recognition with TensorFlow
UPC funded by Facebook, in which a module of Speech Recognition is required. The main contributions of this thesis is providing a speech recognition system trained end-to-end available for integration. This thesis has been written thinking that could be used for other students or developers in the future to participate in upcoming challenges1.
PDF Acoustic modeling for speech recognition under limited training data
Acoustic modeling for speech recognition under limited training data conditions Do, Van Hai 2015 Do, V. H. (2015). ... thesis extends the studies to use context-dependent triphone states as the units to achieve higher acoustic resolution. In addition, linear and nonlinear mapping models with dif- ...
PDF Speech Representation Models for Speech Synthesis and Multimodal Speech
The eld of speech recognition has seen steady advances over the last two decades, leading to the accurate, real-time recognition systems available on mobile phones today. In this thesis, I apply speech modeling techniques developed for recognition to two other speech problems: speech synthesis and multimodal speech recognition with images.
PDF TOWARDS DEEP LEARNING ON SPEECH RECOGNITION FOR A Thesis
In order to perform speech recognition well, a huge amount of transcribed speech and textual data in the target language must be available for system training. The high demand for language resources constrains the development of speech recognition systems for new languages. In this thesis the development of a low-resourced isolated-
PDF Continuous speech recognition for people with dysarthria
cus on dysarthric speech recognition research has not moved from isolated word to more challenging connected speech scenarios yet. There is a clear need to improve continuous dysarthric speech recognition. This thesis is the first to systematically investigate various methods for con-tinuous dysarthric speech recognition.
(PDF) The Implementation and Evaluation of a Speech Recognition
Abstract. Automatic speech recognition is a technology which has a m yriad of real-. world applications. From automatic answering mac hine services, to directory. enquiries and automated delivery ...
PDF Multilingual Techniques for Low Resource Automatic Speech Recognition
about 100 languages with Automatic Speech Recognition (ASR) capability. This is due to the fact that a vast amount of resources is required to build a speech recog-nizer. This often includes thousands of hours of transcribed speech data, a phonetic pronunciation dictionary or lexicon which spans all words in the language, and a text
PDF Deep Learning for Distant Speech Recognition
ligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the eld. This thesis addresses the latter scenario and proposes some novel tech-niques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We rst elaborate on methodologies for realistic
(PDF) Speech Recognition Using Deep Neural Networks: A ...
Download full-text PDF Download full-text PDF Read full-text. ... especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related ...
PDF Developing a Speech Recognition System for Recognizing Tonal Speech
language speech recognition was performed using the Kaldi toolkit. The results were extended to the uncommon speech signals of Gurbani recitation, along with background music, which also reveals this study s novelty and additional contribution to the field of speech recognition [4 7]. The main objective of the work was achieved by adopting and
Towards Evaluating the Robustness of Automatic Speech Recognition
thesize adversarial examples based on Text-to-Speech (TTS) syn-thesis audio. However, style modifications based on optimization objectives significantly reduce the controllability and editability of ... automatic speech recognition and speaker identification systems. In2021 IEEE symposium on security and privacy (SP), pages 730-747. IEEE, 2021.
PDF Developing a Speech Emotion Recognition Solution Using Ensemble
In this thesis work, a robust speech emotion recognition system has been developed to be used by children with autism spectrum disorder (ASD). Children with ASD have difficulty identifying human emotions during social interactions, and the goal of this work was to develop a tool that could be used by these children to better
Dissertations / Theses: 'Speech recognition'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles. Consult the top 50 dissertations / theses for your research on the topic 'Speech recognition.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the ...
arXiv:2405.10025v1 [cs.CL] 16 May 2024
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models Yuchen Hu 1, Chen Chen , Chengwei Qin , Qiushi Zhu2, Eng Siong Chng1, Ruizhe Li3* 1Nanyang Technological University, Singapore 2University of Science and Technology of China, China 3University of Aberdeen, UK [email protected], [email protected]
(PDF) Speech Emotion Recognition: Methods and Cases Study
Download full-text PDF Read full-text. Download full-text PDF. ... PhD thesis. Lim, W., Jang, D., and Lee, T. (2017). ... One of the popular research domains in Automatic Speech Recognition (ASR ...
PDF College of Health and Human Services
B.A., Speech-Language Pathology Melanie Gage Department of Kinesiology B.S., Community Recreation, Youth Development, and Senior Services Outstanding Master's Degree Project Monica Solis Department Social Work Education M.S.W., Master of Social Work "Exploring the Integration of Gut-Brain Axis Insights Within Clinical Social Work Practice".
IMAGES
VIDEO
COMMENTS
This thesis comes from a close collaboration with my advisor, Andrew Ng. Andrew has been an amazing mentor in the process of planning and solving research problems, and constantly encouraged me to work on challenging, impactful problems. Much of the work on speech recognition in this thesis comes from close collab-oration with Dan Jurafsky.
This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art ...
In this thesis, we study attention-based sequence-to-sequence ASR models and address the aforementioned issues. We investigate recurrent neural network (RNN) encoder-decoder models and self-attention encoder-decoder models. For RNN encoder- decoder models, we develop a dynamic subsampling RNN (dsRNN) encoder to shorten the lengths of the input ...
of this approach in a series of diverse case studies in speech recognition, computational chemistry, and natural language processing. Throughout these studies, I extend and modify the neural network models as needed to be more e ective for each task. In the area of speech recognition, I develop a more accurate acoustic model using a deep neural ...
AUTOMATIC SPEECH RECOGNITION by Vimal Manohar A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of ... In the second part of this thesis, we investigate using lattice-based supervi-sion as numerator graph to incorporate uncertainties in unsupervised data in
Many speech recognition applications will bene t from distant-talking speech capture. This avoids problems caused by using hand-held or body-worn equipment. However, due to the large speaker-to-microphone distance, both background noise and reverberant noise will signi cantly corrupt speech signals and negatively impact speech recognition ...
robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in different acoustical environments, and when a desk-top microphone (rather than a close-talking microphone) is used for ...
ABSTRACT. In this thesis, speech recognition systems are developed. The se applications are medium-sized, discrete and individual-. dependent systems. In these sy stems, training and testing ...
remains a challenge. In this thesis, speech feature enhancement and model adaptation for robust speech recognition is studied, and three novel methods to improve performance are introduced. The rst work proposes a modi cation of the spectral subtraction method to reduce the non-stationary characteristics of additive noise in the speech.
Automatic speech recognition (ASR) decodes speech signals into text. While ASR can pro-duce accurate word recognition in clean environments, system performance can degrade dramatically when noise and reverberation are present. In this thesis, speech denoising and model adaptation for robust speech recognition were studied, and four novel meth-
UPC funded by Facebook, in which a module of Speech Recognition is required. The main contributions of this thesis is providing a speech recognition system trained end-to-end available for integration. This thesis has been written thinking that could be used for other students or developers in the future to participate in upcoming challenges1.
Acoustic modeling for speech recognition under limited training data conditions Do, Van Hai 2015 Do, V. H. (2015). ... thesis extends the studies to use context-dependent triphone states as the units to achieve higher acoustic resolution. In addition, linear and nonlinear mapping models with dif- ...
The eld of speech recognition has seen steady advances over the last two decades, leading to the accurate, real-time recognition systems available on mobile phones today. In this thesis, I apply speech modeling techniques developed for recognition to two other speech problems: speech synthesis and multimodal speech recognition with images.
In order to perform speech recognition well, a huge amount of transcribed speech and textual data in the target language must be available for system training. The high demand for language resources constrains the development of speech recognition systems for new languages. In this thesis the development of a low-resourced isolated-
cus on dysarthric speech recognition research has not moved from isolated word to more challenging connected speech scenarios yet. There is a clear need to improve continuous dysarthric speech recognition. This thesis is the first to systematically investigate various methods for con-tinuous dysarthric speech recognition.
Abstract. Automatic speech recognition is a technology which has a m yriad of real-. world applications. From automatic answering mac hine services, to directory. enquiries and automated delivery ...
about 100 languages with Automatic Speech Recognition (ASR) capability. This is due to the fact that a vast amount of resources is required to build a speech recog-nizer. This often includes thousands of hours of transcribed speech data, a phonetic pronunciation dictionary or lexicon which spans all words in the language, and a text
ligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the eld. This thesis addresses the latter scenario and proposes some novel tech-niques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We rst elaborate on methodologies for realistic
Download full-text PDF Download full-text PDF Read full-text. ... especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related ...
language speech recognition was performed using the Kaldi toolkit. The results were extended to the uncommon speech signals of Gurbani recitation, along with background music, which also reveals this study s novelty and additional contribution to the field of speech recognition [4 7]. The main objective of the work was achieved by adopting and
thesize adversarial examples based on Text-to-Speech (TTS) syn-thesis audio. However, style modifications based on optimization objectives significantly reduce the controllability and editability of ... automatic speech recognition and speaker identification systems. In2021 IEEE symposium on security and privacy (SP), pages 730-747. IEEE, 2021.
In this thesis work, a robust speech emotion recognition system has been developed to be used by children with autism spectrum disorder (ASD). Children with ASD have difficulty identifying human emotions during social interactions, and the goal of this work was to develop a tool that could be used by these children to better
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles. Consult the top 50 dissertations / theses for your research on the topic 'Speech recognition.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the ...
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models Yuchen Hu 1, Chen Chen , Chengwei Qin , Qiushi Zhu2, Eng Siong Chng1, Ruizhe Li3* 1Nanyang Technological University, Singapore 2University of Science and Technology of China, China 3University of Aberdeen, UK [email protected], [email protected]
Download full-text PDF Read full-text. Download full-text PDF. ... PhD thesis. Lim, W., Jang, D., and Lee, T. (2017). ... One of the popular research domains in Automatic Speech Recognition (ASR ...
B.A., Speech-Language Pathology Melanie Gage Department of Kinesiology B.S., Community Recreation, Youth Development, and Senior Services Outstanding Master's Degree Project Monica Solis Department Social Work Education M.S.W., Master of Social Work "Exploring the Integration of Gut-Brain Axis Insights Within Clinical Social Work Practice".