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

Robust Automatic Speech Recognition: A Bridge to Practical Applications

ISBN 9780128023983

Autor: Jinyu Li, Li Deng, Reinhold Haeb-Umbach, Yifan Gong

Wydawca: Elsevier

Dostępność: Dostawa 10-20 dni

Cena: 453,60 zł


ISBN13:      

9780128023983

ISBN10:      

0128023988

Autor:      

Jinyu Li, Li Deng, Reinhold Haeb-Umbach, Yifan Gong

Oprawa:      

Hardback

Rok Wydania:      

2015-10-14

Ilość stron:      

306

Tematy:      


Key Features

 

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

 

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 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

 

Readership

 

Researchers and engineers in the area of speech processing, both in industry and academia; Undergraduate and graduate students in the area of signal and speech processing

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