Robust Automatic Speech Recognition: A Bridge to Practical Applications

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9780128023983: Robust Automatic Speech Recognition: A Bridge to Practical Applications

Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid and consistent foundation for noise-robust automatic speech recognition (ASR) and provides a thorough overview of modern noise-robust techniques which have been developed over the past 30 years.

The book emphasizes practical methods that are proven to be successful, also discussing those that are likely to be developed further for future applications. In addition, the pros and cons of using noise-robust ASR techniques for different applications are given, providing users with a practical guide to selecting the best methods for future applications.

  • Connects noise-robust speech recognition methods to machine learning technologies
  • Contains a unified, state-of-the-art survey of successful noise robust speech recognition technologies
  • Provides several ways to classify noise-robust speech recognition technologies into different categories
  • Authored by leading researchers at Microsoft

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From the Back Cover:

Learn how Automatic Speech Recognition (ASR) can be used in real-world applications

This book establishes a solid and consistent foundation for noise-robust Automatic Speech Recognition (ASR) and provides a thorough overview of modern noise-robust techniques which have been developed over the past thirty years.

This book emphasises practical methods that are proven to be successful, and which are likely to develop further for future applications. The pros and cons of using noise-robust ASR techniques for different applications are given, providing a practical guide to selecting the best methods for future applications. Key Features:

  • Connects noise-robust speech recognition methods to machine learning technologies.
  • Provides several ways to classify noise-robust speech recognition technologies into different categories.
  • Written by leading researchers at Microsoft.

With this book you will:

  • Gain a unified understanding of the state-of-the-art technologies for robust speech recognition (ASR)
  • Be able to use the categories defined to guide your technology development
  • Be able to develop noise-robust speech recognition methods in the era of deep learning

About the Author:

Jinyu Li received the Ph.D. degree from Georgia Institute of Technology, Atlanta, in 2008. From 2000 to 2003, he was a Researcher in the Intel China Research Center and Research Manager in iFlytek Speech, China. Currently, he is a Principal Scientist Lead in Microsoft Corporation, Redmond, WA. He Leads a team to design and improve speech modeling algorithms and technologies that ensure industry state-of-the-art speech recognition accuracy for Microsoft products. His major research interests cover several topics in speech recognition, including noise robustness, discriminative training, feature extraction, and machine learning methods.

Li Deng received Ph.D. degree from the University of Wisconsin-Madison. He was an assistant professor (1989-1992), tenured associate professor (1992-1996), and tenured Full Professor (1996-1999) at the University of Waterloo, Canada. In 1999, he joined Microsoft Research, where he is currently a Principal Researcher. He has published over 300 refereed papers in leading journals and conferences in the areas of audio/speech/language technology, machine learning, and signal/information processing. He is a Fellow of the Acoustical Society of America, the IEEE, and the International Speech Communication Association. He served as Editor-in-Chief for the IEEE Signal Processing Magazine (2009-2011). He recently served as General Chair of the IEEE ICASSP-2013, and currently serves as Editor-in-Chief for the IEEE Transactions on Audio, Speech and Language Processing.

Yifan Gong served the National Scientific Research Center (CNRS) and INRIA, France, as Research Engineer and then joined CNRS as Senior Research Scientist. He was a Visiting Research Fellow at the Communications Research Center of Canada. As Senior Member of Technical Staff, he worked for Texas Instruments at the Speech Technologies Lab, where he developed speech modeling technologies robust against noisy environments, designed systems, algorithms, and software for speech and speaker recognition, and delivered memory- and CPU-efficient recognizers for mobile devices. Yifan joined Microsoft in 2004, and is currently a Principal Science Manager in the areas of speech modeling, computing infrastructure, and speech model development for speech products. His research interests include automatic speech recognition/interpretation, signal processing, algorithm development, and engineering process/infrastructure and management. He has authored over 130 publications and awarded over 30 patents. Specific contributions include stochastic trajectory modeling, source normalization HMM training, joint compensation of additive and convolutional noises, and variable parameter HMM. In these areas, he gave tutorials and presentations in international conferences. He has been serving as member of technical committee and session chair for many international conferences, and with IEEE Signal Processing Spoken Language Technical Committees from 1998 to 2002 and since 2013.

Reinhold Häb-Umbach is a professor with the University of Paderborn, Germany. His main research interests are in the fields of statistical signal processing and pattern recognition, with applications to speech enhancement, acoustic beamforming and source separation, as well as automatic speech recognition. After having worked in industrial research laboratories for more than 10 years he joined academia as a full professor of Communications Engineering in 2001. He has published more than 150 papers in peer reviewed journals and conferences.

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Book Description Elsevier Science Publishing Co Inc. Hardback. Book Condition: new. BRAND NEW, Robust Automatic Speech Recognition: A Bridge to Practical Applications, Jinyu Li, Li Deng, Reinhold Haeb-Umbach, Yifan Gong, Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications. The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided. The reader will: * Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition* Learn the links and relationship between alternative technologies for robust speech recognition * Be able to use the technology analysis and categorization detailed in the book to guide future technology development* Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition * The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks* Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment* Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques * Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years. Bookseller Inventory # B9780128023983

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Book Description Elsevier Science Publishing Co Inc, United States, 2015. Hardback. Book Condition: New. 235 x 190 mm. Language: English . Brand New Book. Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications. The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided. The reader will: * Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition* Learn the links and relationship between alternative technologies for robust speech recognition * Be able to use the technology analysis and categorization detailed in the book to guide future technology development* Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition. Bookseller Inventory # AAW9780128023983

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