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Robust automatic speech recognition : a bridge to practical applications /
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 hav...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Academic Press, an imprint of Elsevier,
[2016]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Li, Jinyu, |e author. | |
| 245 | 1 | 0 | |a Robust automatic speech recognition : |b a bridge to practical applications / |c Jinyu Li, Li Deng, Reinhold Haeb-Umbach, Yifang Gong. |
| 264 | 1 | |a Waltham, MA : |b Academic Press, an imprint of Elsevier, |c [2016] | |
| 264 | 4 | |c Ã2016 | |
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed November 5, 2015). | |
| 520 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Automatic speech recognition. | |
| 650 | 0 | |a Speech processing systems. | |
| 650 | 6 | |a Reconnaissance automatique de la parole. | |
| 650 | 6 | |a Traitement automatique de la parole. | |
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| 650 | 7 | |a Automatic speech recognition |2 fast | |
| 650 | 7 | |a Speech processing systems |2 fast | |
| 700 | 1 | |a Deng, Li, |d 1958- |e author. | |
| 700 | 1 | |a Haeb-Umbach, Reinhold, |e author. | |
| 700 | 1 | |a Gong, Yifan, |e author. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780128026168/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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