Robust Automatic Speech Recognition
A Bridge to Practical Applications
Nonfiction, Science & Nature, Technology, Acoustics & Sound, Electronics
| Author: | Jinyu Li, Li Deng, Reinhold Haeb-Umbach, Yifan Gong | ISBN: | 9780128026168 |
| Publisher: | Elsevier Science | Publication: | October 30, 2015 |
| Imprint: | Academic Press | Language: | English |
| Author: | Jinyu Li, Li Deng, Reinhold Haeb-Umbach, Yifan Gong |
| ISBN: | 9780128026168 |
| Publisher: | Elsevier Science |
| Publication: | October 30, 2015 |
| Imprint: | Academic Press |
| Language: | English |
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
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
