New Era for Robust Speech Recognition Exploiting Deep Learning
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, inclu
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Era for Robust Speech Recognition Exploiting Deep Learning
New Era for Robust Speech Recognition
Shinji Watanabe • Marc Delcroix • Florian Metze • John R. Hershey Editors
New Era for Robust Speech Recognition Exploiting Deep Learning
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Editors Shinji Watanabe Mitsubishi Electric Research Laboratories (MERL) Cambridge Massachusetts, USA
Marc Delcroix NTT Communication Science Laboratories NTT Corporation Kyoto, Japan
Florian Metze Language Technologies Institute Carnegie Mellon University Pittsburgh Pennsylvania, USA
John R. Hershey Mitsubishi Electric Research Laboratories (MERL) Cambridge Massachusetts, USA
ISBN 978-3-319-64679-4 DOI 10.1007/978-3-319-64680-0
ISBN 978-3-319-64680-0 (eBook)
Library of Congress Control Number: 2017955274 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
This book is dedicated to the memory of Yajie Miao ( 2016), who tragically passed away during the preparation of this book.
Preface
The field of automatic speech recognition has evolved greatly since the introduction of deep learning, which began only about 5 years ago. In particular, as more and more products using speech recognition are being deployed, there is a crucial need for increased noise robustness, which is well served by deep learning methods. This book covers the state of the art in noise robustness for deep-neural-networkbased speech recognition with a focus on applications to distant speech. Some of the main actors in the areas of front-end and back-end research on noise-robust speech recognition research gathered in Seattle for the 2015 Jelinek Speech and Language Summer Workshop. The
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