國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

New era for robust speech recognitio...

SpringerLink (Online service)

New era for robust speech recognitionexploiting deep learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New era for robust speech recognitionedited 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.
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$q(electronic bk.)

New era for robust speech recognitionexploiting 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$q(electronic bk.)

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

184258
Automatic speech recognition.

LC Class. No.: TK7895.S65

Dewey Class. No.: 006.454

New era for robust speech recognitionexploiting deep learning /
LDR:02356nmm a2200325 a 4500 001 523320
003 DE-He213
005 20180420151358.0
006 m d
007 cr nn 008maaau
008 180628s2017 gw s 0 eng d
020 $a 9783319646800$q(electronic bk.)
020 $a 9783319646794$q(paper)
024 7 $a 10.1007/978-3-319-64680-0 $2 doi
035 $a 978-3-319-64680-0
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7895.S65
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.454 $2 23
090 $a TK7895.S65 $b N532 2017
245 0 0 $a New era for robust speech recognition $h [electronic resource] : $b exploiting deep learning / $c edited by Shinji Watanabe ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xvii, 436 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Automatic speech recognition. $3 184258
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Language Translation and Linguistics. $3 274469
650 2 4 $a Linguistics, general. $3 739809
700 1 $a Watanabe, Shinji. $3 794443
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-64680-0
950 $a Computer Science (Springer-11645)