New Era for Robust Speech Recognition

Exploiting Deep Learning

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Science & Nature, Technology, Electronics, General Computing
Cover of the book New Era for Robust Speech Recognition by , Springer International Publishing
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Author: ISBN: 9783319646800
Publisher: Springer International Publishing Publication: October 30, 2017
Imprint: Springer Language: English
Author:
ISBN: 9783319646800
Publisher: Springer International Publishing
Publication: October 30, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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