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New Era for Robust Speech Recognition - Exploiting Deep Learning (Paperback, Softcover reprint of the original 1st ed. 2017):... New Era for Robust Speech Recognition - Exploiting Deep Learning (Paperback, Softcover reprint of the original 1st ed. 2017)
Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey
R5,300 Discovery Miles 53 000 Ships in 10 - 15 working days

This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.

New Era for Robust Speech Recognition - Exploiting Deep Learning (Hardcover, 1st ed. 2017): Shinji Watanabe, Marc Delcroix,... New Era for Robust Speech Recognition - Exploiting Deep Learning (Hardcover, 1st ed. 2017)
Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey
R6,859 Discovery Miles 68 590 Ships in 10 - 15 working days

This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.

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