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New era for robust speech recognitio...

Watanabe, Shinji.

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New era for robust speech recognition = exploiting deep learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New era for robust speech recognition/ edited by Shinji Watanabe ... [et al.].
其他題名:	exploiting deep learning /
其他作者:	Watanabe, Shinji.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xvii, 436 p. :ill., digital ;24 cm.
內容註:	Speech and Language Processing -- Automatic Speech Recognition (ASR) -- Recent Applications -- Signal-Processing-Based Front-End for Robust ASR -- Generative Model-Based Speech Enhancement -- Denoising Autoencoder -- Discriminative Microphone Array Enhancement -- Learning Robust Feature Representation -- Training Data Augmentation -- Adaptation and Augmented Features -- Novel Model Topologies -- Novel Objective Criteria -- Benchmark Data, Tools, and Systems.
Contained By:	Springer eBooks
標題:	Automatic speech recognition. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-64680-0
ISBN:	9783319646800

New era for robust speech recognition = exploiting deep learning /
New era for robust speech recognitionexploiting deep learning /[electronic resource] :edited by Shinji Watanabe ... [et al.]. - Cham :Springer International Publishing :2017. - xvii, 436 p. :ill., digital ;24 cm.

Speech and Language Processing -- Automatic Speech Recognition (ASR) -- Recent Applications -- Signal-Processing-Based Front-End for Robust ASR -- Generative Model-Based Speech Enhancement -- Denoising Autoencoder -- Discriminative Microphone Array Enhancement -- Learning Robust Feature Representation -- Training Data Augmentation -- Adaptation and Augmented Features -- Novel Model Topologies -- Novel Objective Criteria -- Benchmark Data, Tools, and Systems.

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.

ISBN: 9783319646800

Standard No.: 10.1007/978-3-319-64680-0doiSubjects--Topical Terms:

753709
Automatic speech recognition.

LC Class. No.: TK7895.S65

Dewey Class. No.: 006.454

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505 0 $a Speech and Language Processing -- Automatic Speech Recognition (ASR) -- Recent Applications -- Signal-Processing-Based Front-End for Robust ASR -- Generative Model-Based Speech Enhancement -- Denoising Autoencoder -- Discriminative Microphone Array Enhancement -- Learning Robust Feature Representation -- Training Data Augmentation -- Adaptation and Augmented Features -- Novel Model Topologies -- Novel Objective Criteria -- Benchmark Data, Tools, and Systems.
520 $a 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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