Robust Automatic Speech Recognition

Robust Automatic Speech Recognition : A Bridge to Practical Applications

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Description

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 recognitionLearn 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 developmentBe 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 networksConnects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatmentProvides 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
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Product details

  • Electronic book text | 308 pages
  • 191 x 235mm
  • Academic Press Inc
  • United States
  • English
  • 9780128026168

Table of contents

Introduction   Fundamental of speech recognition         Background of robust speech recognition
Processing in the Feature and Model DomainsCompensation with prior knowledge   Explicit distortion modeling     Uncertainty processing            Joint model training     Reverberant speech recognition         Multi-channel processing   Summary and Future Directions
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