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Published by Springer International Publishing AG, CH, 2018
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Kartoniert / Broschiert. Condition: New. Field of automatic speech recognition has evolved greatly since the introduction of deep learningCovers the state-of-the-art in noise robustness for deep neural-network-based speech recognitionIncludes descrip.
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Taschenbuch. Condition: Neu. New Era for Robust Speech Recognition | Exploiting Deep Learning | Shinji Watanabe (u. a.) | Taschenbuch | xvii | Englisch | 2018 | Springer | EAN 9783319878492 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: Hervorragend. Zustand: Hervorragend | Seiten: 456 | Sprache: Englisch | Produktart: Bücher | 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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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Field of automatic speech recognition has evolved greatly since the introduction of deep learningCovers the state-of-the-art in noise robustness for deep neural-network-based speech recognitionIncludes descrip.
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Published by Springer International Publishing Mai 2018, 2018
ISBN 10: 3319878492 ISBN 13: 9783319878492
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 456 pp. Englisch.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 456 pp. Englisch.