Overview
- Scientists and researchers in the field of speech recognition will find an overview of the state of the art in robust speech recognition.
- Professionals working in speech recognition will find strategies for improving results in various conditions of mismatch.
- The contributing authors are among the leading researchers in this field.
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Table of contents (13 chapters)
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Front Matter
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Theoretical Foundations
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Front Matter
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Applications: Noise Robustness
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Front Matter
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Applications: Reverberation Robustness
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Front Matter
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Applications: Multiple Speakers and Modalities
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Front Matter
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Back Matter
Keywords
About this book
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 that are most reliable for recognition. This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition.
The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.
Editors and Affiliations
About the editors
Prof. Dr.-Ing. Dorothea Kolossa is a professor at the Institut für Kommunikationsakustik of the Ruhr-Universität Bochum, Germany; her research interests are automatic speech recognition, digital speech signal processing, and blind source separation.
Prof. Dr.-Ing. Reinhold Haeb-Umbach heads the Dept. of Communications Engineering of the University of Paderborn, Germany; his research interest are speech signal processing and automatic speech recognition, statistical learning and pattern recognition, and signal processing for digital communications.
Bibliographic Information
Book Title: Robust Speech Recognition of Uncertain or Missing Data
Book Subtitle: Theory and Applications
Editors: Dorothea Kolossa, Reinhold Häb-Umbach
DOI: https://doi.org/10.1007/978-3-642-21317-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2011
Hardcover ISBN: 978-3-642-21316-8Published: 14 July 2011
Softcover ISBN: 978-3-642-43868-4Published: 12 November 2014
eBook ISBN: 978-3-642-21317-5Published: 14 July 2011
Edition Number: 1
Number of Pages: XVIII, 380
Topics: Signal, Image and Speech Processing, Artificial Intelligence, Computational Linguistics