![New era for robust speech recognition [electronic resource] : exploiting deep learning](https://image.aladin.co.kr/product/11292/78/cover/3319646796_2.jpg)
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| 001 | 000045989740 | |
| 005 | 20190719101508 | |
| 006 | m d | |
| 007 | cr | |
| 008 | 190708s2017 sz a ob 001 0 eng d | |
| 020 | ▼a 9783319646794 | |
| 020 | ▼a 9783319646800 (e-book) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 050 | 4 | ▼a Q334-342 |
| 082 | 0 4 | ▼a 006.454 ▼2 23 |
| 084 | ▼a 006.454 ▼2 DDCK | |
| 090 | ▼a 006.454 | |
| 245 | 0 0 | ▼a New era for robust speech recognition ▼h [electronic resource] : ▼b exploiting deep learning / ▼c Shinji Watanabe ... [et al.], editors. |
| 260 | ▼a Cham : ▼b Springer, ▼c c2017. | |
| 300 | ▼a 1 online resource (xvii, 436 p.) : ▼b ill. | |
| 500 | ▼a Title from e-Book title page. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 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. | |
| 530 | ▼a Issued also as a book. | |
| 538 | ▼a Mode of access: World Wide Web. | |
| 650 | 0 | ▼a Computer science. |
| 650 | 0 | ▼a Artificial intelligence. |
| 650 | 0 | ▼a Computational linguistics. |
| 700 | 1 | ▼a Watanabe, Shinji. |
| 856 | 4 0 | ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-64680-0 |
| 945 | ▼a KLPA | |
| 991 | ▼a E-Book(소장) |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/e-Book 컬렉션/ | 청구기호 CR 006.454 | 등록번호 E14015282 | 도서상태 대출불가(열람가능) | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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 feature
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.
정보제공 :
목차
Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- Part I Introduction -- 1 Preliminaries -- 1.1 Introduction -- 1.1.1 Motivation -- 1.1.2 Before the Deep Learning Era -- 1.1.2.1 Feature Space Approaches -- 1.1.2.2 Model Space Approaches -- 1.2 Basic Formulation and Notations -- 1.2.1 General...