Editors:
Field of automatic speech recognition has evolved greatly since the introduction of deep learning
Covers the state-of-the-art in noise robustness for deep neural-network-based speech recognition
Includes descriptions of benchmark tools and datasets widely used in the field
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (20 chapters)
-
Front Matter
-
Introduction
-
Front Matter
-
-
Approaches to Robust Automatic Speech Recognition
-
Front Matter
-
About this book
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.Â
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.
Editors and Affiliations
-
Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA
Shinji Watanabe, John R. Hershey
-
NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan
Marc Delcroix
-
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA
Florian Metze
Bibliographic Information
Book Title: New Era for Robust Speech Recognition
Book Subtitle: Exploiting Deep Learning
Editors: Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey
DOI: https://doi.org/10.1007/978-3-319-64680-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-64679-4Published: 10 November 2017
Softcover ISBN: 978-3-319-87849-2Published: 24 May 2018
eBook ISBN: 978-3-319-64680-0Published: 30 October 2017
Edition Number: 1
Number of Pages: XVII, 436
Number of Illustrations: 50 b/w illustrations, 26 illustrations in colour
Topics: Artificial Intelligence, Signal, Image and Speech Processing, Natural Language Processing (NLP), Linguistics, general