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6 books on Speech Recognition [PDF]
Updated: May 15, 2024 | 20 |
Books on Speech Recognition serve as crucial references for startups dedicated to developing speech recognition technologies. These resources offer a comprehensive foundation in the field, covering various aspects of automatic speech recognition, from acoustic modeling and language modeling to voice assistant systems and speech-to-text conversion. They delve into the complexities of understanding and transcribing spoken language, offering insights into deep learning techniques and neural networks. Moreover, these books often provide practical examples, datasets, and best practices, enabling startups to fine-tune their speech recognition algorithms for higher accuracy and real-world applications.
1. Deep Learning for NLP and Speech Recognition
2019 by Uday Kamath, John Liu, James Whitaker
In "Deep Learning for NLP and Speech Recognition," this textbook offers a detailed exploration of deep learning architecture and its practical applications across various Natural Language Processing (NLP) tasks, encompassing Document Classification, Machine Translation, Language Modeling, and Speech Recognition. Given the widespread integration of deep learning, NLP, and speech applications across diverse domains such as Finance, Healthcare, and Government, there arises an increasing demand for a comprehensive resource that effectively bridges deep learning techniques with NLP and speech, offering practical insights on leveraging these tools and libraries for real-world scenarios. This book not only elucidates contemporary deep learning methods relevant to NLP and speech but also presents cutting-edge approaches and incorporates hands-on experience through real-world case studies and accompanying code samples.
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2. Speech Recognition Using Articulatory and Excitation Source Features
2017 by K. Sreenivasa Rao, Manjunath K E
In "Speech Recognition Using Articulatory and Excitation Source Features," the authors delve into the role of articulatory and excitation source information in the differentiation of sound units. Their primary focus centers on the excitation source aspect of speech and the dynamic behavior of various articulators during the process of speech production, all aimed at bolstering the performance of speech recognition (SR). The book scrutinizes SR across diverse speech modes, encompassing scripted, spontaneous, and conversational speech. It explores five distinct groups of articulatory features (AFs), supplementing conventional spectral features, with each chapter offering a rationale for the chosen feature's relevance in the SR task, elucidating extraction methodologies, and proposing suitable models for capturing sound unit-specific insights from these novel features. The book culminates by examining potential combinations of spectral, articulatory, and source features alongside the requisite models to elevate the efficiency of SR systems.
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3. Automatic Speech Recognition: A Deep Learning Approach
2014 by Dong Yu, Li Deng
"Automatic Speech Recognition: A Deep Learning Approach" offers a thorough examination of the recent strides in automatic speech recognition, with a primary emphasis on deep learning models, including various iterations of deep neural networks. Remarkably, this book stands as the inaugural work dedicated exclusively to the deep learning perspective within automatic speech recognition. Beyond its rigorous mathematical exposition, the book delves into the underlying insights and theoretical underpinnings of a range of exceptionally effective deep learning models in this field.
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4. Advances in Speech Recognition: Mobile Environments, Call Centers and Clinics
2010 by Amy Neustein
When Amy invited me to co-author the foreword for her latest book on advancements in speech recognition, I felt truly honored. Amy's work has always been marked by her creative vigor, and I anticipated that her book would captivate both seasoned speech professionals and newcomers to the field of speech processing. Collaborating on this foreword with Bill Scholz added an extra layer of enjoyment to the task. I have known Bill since his tenure at UNISYS, where he oversaw projects that significantly shaped speech recognition tools and applications. Bill Scholz and I have been impressed by the insights and analyses presented by the authors Amy has assembled for this collection. They have shed light on numerous substantial contributions to the field. The book demonstrates that speech recognition has evolved beyond the realm of experimental future technology; it is a practical reality today, equipped with the capabilities to tackle even the most challenging tasks. Furthermore, the traditional point-click-type graphical user interface (GUI) approach is no longer sufficient, particularly given the constraints of modern handheld devices. Instead, we are witnessing the integration of voice user interfaces (VUI) and GUI into unified multimodal solutions that are evolving into the fundamental paradigm for human-computer interaction in the future.
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5. Automatic Speech Recognition on Mobile Devices and over Communication Networks
2008 by Zheng-Hua Tan, Boerge Lindberg
The significant strides in computing and networking have ignited a profound fascination with implementing automatic speech recognition on mobile devices and across communication networks, and this trajectory is gaining momentum. "Automatic Speech Recognition on Mobile Devices and over Communication Networks" assembles leading experts from academia and industry to delve into the challenges of this burgeoning domain, providing readers with a comprehensive introduction to speech recognition within the context of devices and networks. The book encompasses network, distributed, and embedded speech recognition systems, all of which are anticipated to coexist in the evolving landscape. It presents a comprehensive and unified perspective on the subject, highlighting the latest developments, current standards, and existing off-the-shelf systems.
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6. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
2000 by Dan Jurafsky, James H. Martin
This book offers an empirical perspective on language processing by employing statistical and machine-learning algorithms in practical contexts, utilizing large-scale datasets as a foundation. Each chapter is constructed around detailed worked examples that serve to elucidate the central concepts and illuminate the relative merits and drawbacks of different approaches. Methodology boxes are thoughtfully integrated throughout, introducing essential tools like evaluation methods and the wizard of oz techniques. Each chapter also features problem sets for hands-on reinforcement of knowledge. Furthermore, the book breaks down the traditional boundaries between speech and text processing, merging these domains seamlessly. It comprehensively covers empiricist, statistical, and machine learning methodologies in language processing, encompassing both contemporary statistical approaches and earlier rule-based methods. The inclusion of modern and stringent evaluation metrics ensures readers are well-prepared for assessing language processing systems. This comprehensive resource unifies fundamental algorithms from various language processing domains, demonstrating their applicability across spoken and written language tasks, including speech recognition and word-sense disambiguation. Emphasizing the evolving landscape of the web and other platforms, the book provides a contemporary perspective on the field of language processing.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Deep Learning for NLP and Speech Recognition
2019 by Uday Kamath, John Liu, James Whitaker
In "Deep Learning for NLP and Speech Recognition," this textbook offers a detailed exploration of deep learning architecture and its practical applications across various Natural Language Processing (NLP) tasks, encompassing Document Classification, Machine Translation, Language Modeling, and Speech Recognition. Given the widespread integration of deep learning, NLP, and speech applications across diverse domains such as Finance, Healthcare, and Government, there arises an increasing demand for a comprehensive resource that effectively bridges deep learning techniques with NLP and speech, offering practical insights on leveraging these tools and libraries for real-world scenarios. This book not only elucidates contemporary deep learning methods relevant to NLP and speech but also presents cutting-edge approaches and incorporates hands-on experience through real-world case studies and accompanying code samples.
Download PDF
2. Speech Recognition Using Articulatory and Excitation Source Features
2017 by K. Sreenivasa Rao, Manjunath K E
In "Speech Recognition Using Articulatory and Excitation Source Features," the authors delve into the role of articulatory and excitation source information in the differentiation of sound units. Their primary focus centers on the excitation source aspect of speech and the dynamic behavior of various articulators during the process of speech production, all aimed at bolstering the performance of speech recognition (SR). The book scrutinizes SR across diverse speech modes, encompassing scripted, spontaneous, and conversational speech. It explores five distinct groups of articulatory features (AFs), supplementing conventional spectral features, with each chapter offering a rationale for the chosen feature's relevance in the SR task, elucidating extraction methodologies, and proposing suitable models for capturing sound unit-specific insights from these novel features. The book culminates by examining potential combinations of spectral, articulatory, and source features alongside the requisite models to elevate the efficiency of SR systems.
Download PDF
3. Automatic Speech Recognition: A Deep Learning Approach
2014 by Dong Yu, Li Deng
"Automatic Speech Recognition: A Deep Learning Approach" offers a thorough examination of the recent strides in automatic speech recognition, with a primary emphasis on deep learning models, including various iterations of deep neural networks. Remarkably, this book stands as the inaugural work dedicated exclusively to the deep learning perspective within automatic speech recognition. Beyond its rigorous mathematical exposition, the book delves into the underlying insights and theoretical underpinnings of a range of exceptionally effective deep learning models in this field.
Download PDF
4. Advances in Speech Recognition: Mobile Environments, Call Centers and Clinics
2010 by Amy Neustein
When Amy invited me to co-author the foreword for her latest book on advancements in speech recognition, I felt truly honored. Amy's work has always been marked by her creative vigor, and I anticipated that her book would captivate both seasoned speech professionals and newcomers to the field of speech processing. Collaborating on this foreword with Bill Scholz added an extra layer of enjoyment to the task. I have known Bill since his tenure at UNISYS, where he oversaw projects that significantly shaped speech recognition tools and applications. Bill Scholz and I have been impressed by the insights and analyses presented by the authors Amy has assembled for this collection. They have shed light on numerous substantial contributions to the field. The book demonstrates that speech recognition has evolved beyond the realm of experimental future technology; it is a practical reality today, equipped with the capabilities to tackle even the most challenging tasks. Furthermore, the traditional point-click-type graphical user interface (GUI) approach is no longer sufficient, particularly given the constraints of modern handheld devices. Instead, we are witnessing the integration of voice user interfaces (VUI) and GUI into unified multimodal solutions that are evolving into the fundamental paradigm for human-computer interaction in the future.
Download PDF
5. Automatic Speech Recognition on Mobile Devices and over Communication Networks
2008 by Zheng-Hua Tan, Boerge Lindberg
The significant strides in computing and networking have ignited a profound fascination with implementing automatic speech recognition on mobile devices and across communication networks, and this trajectory is gaining momentum. "Automatic Speech Recognition on Mobile Devices and over Communication Networks" assembles leading experts from academia and industry to delve into the challenges of this burgeoning domain, providing readers with a comprehensive introduction to speech recognition within the context of devices and networks. The book encompasses network, distributed, and embedded speech recognition systems, all of which are anticipated to coexist in the evolving landscape. It presents a comprehensive and unified perspective on the subject, highlighting the latest developments, current standards, and existing off-the-shelf systems.
Download PDF
6. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
2000 by Dan Jurafsky, James H. Martin
This book offers an empirical perspective on language processing by employing statistical and machine-learning algorithms in practical contexts, utilizing large-scale datasets as a foundation. Each chapter is constructed around detailed worked examples that serve to elucidate the central concepts and illuminate the relative merits and drawbacks of different approaches. Methodology boxes are thoughtfully integrated throughout, introducing essential tools like evaluation methods and the wizard of oz techniques. Each chapter also features problem sets for hands-on reinforcement of knowledge. Furthermore, the book breaks down the traditional boundaries between speech and text processing, merging these domains seamlessly. It comprehensively covers empiricist, statistical, and machine learning methodologies in language processing, encompassing both contemporary statistical approaches and earlier rule-based methods. The inclusion of modern and stringent evaluation metrics ensures readers are well-prepared for assessing language processing systems. This comprehensive resource unifies fundamental algorithms from various language processing domains, demonstrating their applicability across spoken and written language tasks, including speech recognition and word-sense disambiguation. Emphasizing the evolving landscape of the web and other platforms, the book provides a contemporary perspective on the field of language processing.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
