Robustness in Automatic Speech Recognition: Fundamentals and Applications / Edition 1

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Overview

The domain of speech processing has come to the point where researchers and engineers are concerned with how speech technology can be applied to new products, and how this technology will transform our future. One important problem is to improve robustness of speech processing under adverse conditions, which is the subject of this book.
Robust speech processing is a relatively new area which became a concern as technology started moving from laboratory to field applications. A method or an algorithm is robust if it can deal with a broad range of applications and adapt to unknown conditions. Robustness in Automatic Speech Recognition addresses all of the fundamental problems and issues in the area.
The book is divided into three parts. The first provides the background necessary for understanding the rest of the material. It also emphasizes the problems of speech production and perception in noise along with popular techniques used in speech analysis and automatic speech recognition. Part Two discusses the problems relevant to robustness in automatic speech recognition and speech-based applications. It emphasizes intra- and inter-speaker variability as well as automatic speech recognition of Lombard, noisy and channel distorted speech. Finally, the third part covers recent advances in the field of robust automatic speech recognition.
Audience: An invaluable reference. May be used as a text for advanced courses on the subject.

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Editorial Reviews

Booknews
Provides a unified view of improving the capability of machines to recognize speech so that it will be reliable in the changing and often unpredictable conditions in real use; so far it works very well in the laboratory but not as consistently as consumers demand elsewhere. Covers the problems of speech production and perception in noise, popular techniques used in speech analysis and automatic speech recognition, problems relevant to robustness and speech-based applications, variability between and within speakers, various types of distorted speech, and recent advances in dealing with such problems. Not intended, but suitable for a graduate or undergraduate course. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

About the authors. Foreword. Preface. Part A: Speech communication by humans and machines. 1. Nature and perception of speech sounds. 2. Background on speech analysis. 3. Fundamentals of automatic speech recognition. Part B: Robustness in ASR: Problems and issues. 4. Speaker variability and specificity. 5. Dealing with noisy speech and channel distortions. Part C: Possible solutions and some perspectives. 6. The current technology and its limits: an overview. 7. Towards robust speech analysis. 8. On the use of a robust speech representation. 9. ASR of noisy, stressed, and channel distorted speech. 10. Word-spotting and rejection. 11. Spontaneous speech. 12. On the use of knowledge in ASR. 13. Application domain, human factors, and dialogue. Appendix. Index.

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