Robust automatic speech recognition : a bridge to practical applications

✍ Scribed by Deng, Li; Gong, Yifan; Haeb-Umbach, Reinhold; Li, Jinyu

Publisher: Academic Press
Year: 2016
Tongue: English
Leaves: 298
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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

✦ Table of Contents

Content:
Front Matter,Copyright,About the Authors,List of Figures,List of Tables,Acronyms,NotationsEntitled to full textChapter 1 - Introduction, Pages 1-7
Chapter 2 - Fundamentals of speech recognition, Pages 9-40
Chapter 3 - Background of robust speech recognition, Pages 41-63
Chapter 4 - Processing in the feature and model domains, Pages 65-106
Chapter 5 - Compensation with prior knowledge, Pages 107-136
Chapter 6 - Explicit distortion modeling, Pages 137-170
Chapter 7 - Uncertainty processing, Pages 171-186
Chapter 8 - Joint model training, Pages 187-202
Chapter 9 - Reverberant speech recognition, Pages 203-238
Chapter 10 - Multi-channel processing, Pages 239-260
Chapter 11 - Summary and future directions, Pages 261-280
Index, Pages 281-286

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