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Author: Uday Kamath
Publisher: Springer
ISBN: 3030145964
Category : Computers
Languages : en
Pages : 640
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Book Description
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Author: Uday Kamath
Publisher: Springer
ISBN: 3030145964
Category : Computers
Languages : en
Pages : 640
Get Book Here
Book Description
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Author: Arthur S. House
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 520
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Book Description
This bibliography aims to provide an easily accessible and thus convenient reference source for all those with an interest in automatic speech recognition and speech technology. The coverage is extensive and up-to-date and provides a historical perspective, as well as a reference point for recent work. Whether it is used merely to check references or as a starting point for an exploration of this field of research, this volume will prove an invaluable addition to any personal or institutional library.
Author: Alexander Waibel
Publisher: Elsevier
ISBN: 0080515843
Category : Computers
Languages : en
Pages : 640
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Book Description
After more than two decades of research activity, speech recognition has begun to live up to its promise as a practical technology and interest in the field is growing dramatically. Readings in Speech Recognition provides a collection of seminal papers that have influenced or redirected the field and that illustrate the central insights that have emerged over the years. The editors provide an introduction to the field, its concerns and research problems. Subsequent chapters are devoted to the main schools of thought and design philosophies that have motivated different approaches to speech recognition system design. Each chapter includes an introduction to the papers that highlights the major insights or needs that have motivated an approach to a problem and describes the commonalities and differences of that approach to others in the book.
Author: George W. Hughes
Publisher:
ISBN:
Category : Electronic digital computers
Languages : en
Pages : 76
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Book Description
Author: Roberto Pieraccini
Publisher: MIT Press
ISBN: 0262016850
Category : Computers
Languages : en
Pages : 355
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Book Description
An examination of more than sixty years of successes and failures in developing technologies that allow computers to understand human spoken language. Stanley Kubrick's 1968 film 2001: A Space Odyssey famously featured HAL, a computer with the ability to hold lengthy conversations with his fellow space travelers. More than forty years later, we have advanced computer technology that Kubrick never imagined, but we do not have computers that talk and understand speech as HAL did. Is it a failure of our technology that we have not gotten much further than an automated voice that tells us to "say or press 1"? Or is there something fundamental in human language and speech that we do not yet understand deeply enough to be able to replicate in a computer? In The Voice in the Machine, Roberto Pieraccini examines six decades of work in science and technology to develop computers that can interact with humans using speech and the industry that has arisen around the quest for these technologies. He shows that although the computers today that understand speech may not have HAL's capacity for conversation, they have capabilities that make them usable in many applications today and are on a fast track of improvement and innovation. Pieraccini describes the evolution of speech recognition and speech understanding processes from waveform methods to artificial intelligence approaches to statistical learning and modeling of human speech based on a rigorous mathematical model--specifically, Hidden Markov Models (HMM). He details the development of dialog systems, the ability to produce speech, and the process of bringing talking machines to the market. Finally, he asks a question that only the future can answer: will we end up with HAL-like computers or something completely unexpected?
Author: Priyanka A. Abhang
Publisher: Academic Press
ISBN: 0128045310
Category : Medical
Languages : en
Pages : 200
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Book Description
Introduction to EEG- and Speech-Based Emotion Recognition Methods examines the background, methods, and utility of using electroencephalograms (EEGs) to detect and recognize different emotions. By incorporating these methods in brain-computer interface (BCI), we can achieve more natural, efficient communication between humans and computers. This book discusses how emotional states can be recognized in EEG images, and how this is useful for BCI applications. EEG and speech processing methods are explored, as are the technological basics of how to operate and record EEGs. Finally, the authors include information on EEG-based emotion recognition, classification, and a proposed EEG/speech fusion method for how to most accurately detect emotional states in EEG recordings. - Provides detailed insight on the science of emotion and the brain signals underlying this phenomenon - Examines emotions as a multimodal entity, utilizing a bimodal emotion recognition system of EEG and speech data - Details the implementation of techniques used for acquiring as well as analyzing EEG and speech signals for emotion recognition
Author: Lawrence R. Rabiner
Publisher:
ISBN: 9788129701381
Category : Automatic speech recognition
Languages : en
Pages : 507
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Book Description
Author: Joseph Keshet
Publisher: John Wiley & Sons
ISBN: 9780470742037
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.
Author: for the National Academy of Sciences
Publisher: National Academies Press
ISBN: 0309049881
Category : Technology & Engineering
Languages : en
Pages : 559
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Book Description
Science fiction has long been populated with conversational computers and robots. Now, speech synthesis and recognition have matured to where a wide range of real-world applicationsâ€"from serving people with disabilities to boosting the nation's competitivenessâ€"are within our grasp. Voice Communication Between Humans and Machines takes the first interdisciplinary look at what we know about voice processing, where our technologies stand, and what the future may hold for this fascinating field. The volume integrates theoretical, technical, and practical views from world-class experts at leading research centers around the world, reporting on the scientific bases behind human-machine voice communication, the state of the art in computerization, and progress in user friendliness. It offers an up-to-date treatment of technological progress in key areas: speech synthesis, speech recognition, and natural language understanding. The book also explores the emergence of the voice processing industry and specific opportunities in telecommunications and other businesses, in military and government operations, and in assistance for the disabled. It outlines, as well, practical issues and research questions that must be resolved if machines are to become fellow problem-solvers along with humans. Voice Communication Between Humans and Machines provides a comprehensive understanding of the field of voice processing for engineers, researchers, and business executives, as well as speech and hearing specialists, advocates for people with disabilities, faculty and students, and interested individuals.
Author: Bernd T. Meyer
Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
ISBN: 9783838121550
Category :
Languages : en
Pages : 140
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Book Description
While human listeners have little problems in dealing with the strong variation in spoken language, the same cannot be said about automatic speech recognition (ASR). This work compares recognition performance of man and machine with the aim of learning from the distinct errors between these two. Based on the differences, the signal processing mechanisms are analyzed that are suitable to increase the robustness of ASR. The comparison focuses on the influence of intrinsic variation of speech, i.e., changes in speaking rate, effort and style, as well as dialect and accent. The outcome of the experiments suggests that the processing of temporal cues in ASR bears room for improvement. Therefore, spectro-temporal features are employed as input to ASR systems, which results in an increase of recognition performance for varying speaking effort and speaking style compared to standard features. This documents the usefulness of spectro-temporal and temporal information for automatic recognizers.
