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Techniques for Noise Robustness in Automatic Speech Recognition

Techniques for Noise Robustness in Automatic Speech Recognition

Techniques for Noise Robustness in Automatic Speech Recognition
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Techniques for Noise Robustness in Automatic Speech Recognition Hardback - - 1st Edition

by Tuomas Virtanen (Editor); Rita Singh (Editor); Bhiksha Raj (Editor)

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John Wiley & Sons , pp. 514 Index. Hardback. New.
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  • Title Techniques for Noise Robustness in Automatic Speech Recognition
  • Author Tuomas Virtanen (Editor); Rita Singh (Editor); Bhiksha Raj (Editor)
  • Binding Hardback
  • Edition number 1st
  • Edition 1
  • Condition New
  • Pages 514
  • Volumes 1
  • Language ENG
  • Publisher John Wiley & Sons
  • Publication date pp. 514 Index
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Table of Contents
  • Bookseller's Inventory # 617372020
  • ISBN 9781119970880 / 1119970881
  • Weight 2 lbs (0.91 kg)
  • Dimensions 9.8 x 6.9 x 1.1 in (24.89 x 17.53 x 2.79 cm)
  • Category Technology & Industrial Arts
  • Library of Congress subjects Automatic speech recognition
  • Library of Congress Catalogue Number 2012025172
  • Dewey Decimal Code 006.454
  • Quantity available 1

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From the publisher

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

From the rear cover

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.

About the author

Tuomas Virtanen, Tampere University of Technology, FinlandDr . Virtanen is a senior researcher at Tampere University of Technology. Previously, he has worked at Cambridge University, UK as a research associate. His main research contributions are in sound source separation and its application to robust speech recognition, audio content analysis, and music information retrieval. He is well-known for his work on non-negative matrix factorization based source separation, which is currently widely used in the field. He has published numerous journal and conference articles related to above topics.

Rita Singh, Carnegie Mellon University, USA
Dr. Singh is the CEO of a speech-technology startup but remains an adjunct faculty of the Language Technologies Institute at Carnegie Mellon University. She has been a major contributor to the open-source CMU sphinx and is one of the main architects of the popular Sphinx4 java-based open-source speech recognition system. In addition to her work on core speech recognition technology, she has also developed several algorithms for noise compensation, and was the prime architect of CMU's award-winning submission to the 2001 Naval Research Lab's challenge on automatic recognition of speech in noisy environments (SPINE).

Bhiksha Raj, Carnegie Mellon University, USA
Dr. Raj is an associate professor in the Language Technologies Institute and in Electrical and Computer Engineering at Carnegie Mellon University. He has worked extensively on robustness algorithms for speech recognition, and is very well-known for his contributions to the highly-popular VTS approach for noise compensation, as well as his contributions to missing-feature-based techniques for noise compensation. He has published extensively on and holds patents for algorithms for microphone array processing and signal separation.

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