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Speech technology - the use of speech as a means of sending information to, and receiving information from computer systems has been in use as a research tool for many years. Only recently has it begun to move out of the laboratory and into commercially worthwhile applications, first with compressed and synthesised spoken messages, then with computer recognition of spoken messages, and today with diverse applications involving both recognition and reproduction of human speech. We have written this book because we believe the technology has now advanced to the point where many more applications of voice recognition and response are both feasible and economically attractive. Computers that can understand everyday speech are still a distant prospect, but provided the limitations of present day equipment are clearly understood there is much that can be achieved with it. Our aim is to show, in non-technical language, what is now possible with the help of speech technology. The text includes many examples of current applications in industry, commerce and other fields, and we have selected five current industrial applications combining speech recognition and response for more detailed attention. Industrial cases have been chosen both because we see industry as an important growth area for speech applications in the next few years, and because it presents some of the greatest difficulties in speech recognition - if you can make it work in industry, then you can make it work almost anywhere.
This book gives an overview of the research and application of speech technologies in different areas. One of the special characteristics of the book is that the authors take a broad view of the multiple research areas and take the multidisciplinary approach to the topics. One of the goals in this book is to emphasize the application. User experience, human factors and usability issues are the focus in this book.
Special Features: · Source codes for compiling and implementing ASR algorithms in C++ are included in electronic format on an accompanying CD-ROM· Contains a practical account of the functioning of ASR· Includes implementation-oriented mathematical and technical explanations of ASR· Features a stage-by-stage explanation of how to create an ASR interface· Can be used both for teaching speech recognition techniques and testing and development of new systems on digital signal processing hardware About The Book: Automatic Speech Recognition (ASR) is becoming increasingly prevalent in such applications as private telephone exchanges and real-time on-line telephone information services. This book introduces the principles of ASR systems, including the theory and the implementation issues behind multi-speaker continuous speech ASR. The book supplies the full C++ code to further clarify the implementation details of a typical commercial/laboratory ASR system and to allow the readers to reach practical solutions for ASR-related problems.About the topic/technology Automatic Speech Recognition (ASR) is the technology behind the voice-triggered computer menus. Uses of these systems are now proliferating rapidly and include private telephone exchanges and real-time on-line telephone information services.
In this text, the editors aim to convert the mapping of speech patterns into mental representations. They cover theories of perception and cognition, issues in clinical speech pathology, and the practical concerns of speech technology.
This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.
Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field
Deals with the two important technologies in human-computer interaction, computer generation of synthetic speech and computer recognition of human speech. The book focuses on three main areas - recognition, production and dialogue.
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.
This volume constitutes selected papers presented at the Third International Conference on Artificial Intelligence and Speech Technology, AIST 2021, held in Delhi, India, in November 2021. The 36 full papers and 18 short papers presented were thoroughly reviewed and selected from the 178 submissions. They provide a discussion on application of Artificial Intelligence tools in speech analysis, representation and models, spoken language recognition and understanding, affective speech recognition, interpretation and synthesis, speech interface design and human factors engineering, speech emotion recognition technologies, audio-visual speech processing and several others.
In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.