Robust Speech Recognition of Uncertain or Missing Data Theory and Ap
Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by us
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Dorothea Kolossa
•
Reinhold Haeb-Umbach
Editors
Robust Speech Recognition of Uncertain or Missing Data Theory and Applications
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Editors Prof. Dr.-Ing. Dorothea Kolossa Institute of Communication Acoustics Ruhr-Universit¨at Bochum Universit¨atsstrasse 150 44801 Bochum Germany [email protected]
Prof. Dr.-Ing. Reinhold Haeb-Umbach Department of Communications Engineering University of Paderborn Warburger Strasse 100 33098 Paderborn Germany [email protected]
ISBN 978-3-642-21316-8 e-ISBN 978-3-642-21317-5 DOI 10.1007/978-3-642-21317-5 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011932686 ACM codes: I.2.7, G.3 c Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, 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. Cover design: deblik Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability, to selectively focus on those segments and features which are most reliable for recognition. This book presents the state of the art in recognition of uncertain or missing speech data, presenting examples that utilize uncertainty information for noise robustness, for reverberation robustness and for the simultaneous recognition of multiple speech signals, as well as for audiovisual speech recognition. The editors thank all the authors for their valuable contributions and their cooperation in unifying the layout of the book and the terminology and symbols used. It was a great pleasure working with all of them! Furthermore, the editors would like to express their gratitude to Ronan Nugent of Springer for his encouragement and support during the creation of this book. We also thank Alexander Krueger and Volker Leutnant for their help with the compilation of the LaTeX document. Paderborn and Bochum, February 2011
Reinhold Haeb-Umbach Dorothea Kolossa
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Contents
Abbreviations and Acronyms . . . . . . . . . . . . .
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