Overview
- Presents important theoretical foundation and practical considerations of using a wide range of deep learning models and methods for automatic speech recognition
- Reviews past and present work (up to the fall of year 2014) on most impactful work based on deep learning for acoustic modeling in speech recognition
- Goes deeply into rigorous mathematical and technical descriptions of deep learning methods successful for speech recognition and related areas of applications
- Analyzes research directions and trends towards establishing future-generation speech recognition based on extending the current deep learning models
- Includes supplementary material: sn.pub/extras
Part of the book series: Signals and Communication Technology (SCT)
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Table of contents (15 chapters)
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Front Matter
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Conventional Acoustic Models
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Front Matter
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Deep Neural Networks
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Front Matter
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Deep Neural Network-Hidden Markov Model Hybrid Systems for Automatic Speech Recognition
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Front Matter
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Representation Learning in Deep Neural Networks
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Front Matter
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Advanced Deep Models
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Front Matter
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Keywords
About this book
This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.
Reviews
“The book addresses real-world problems of current interest regarding automatic speech recognition. … This book is useful for all researchers working in automatic speech recognition as well as in real-world applications of deep learning.” (Ruxandra Stoean, zbMATH 1356.68004, 2017)
Authors and Affiliations
Bibliographic Information
Book Title: Automatic Speech Recognition
Book Subtitle: A Deep Learning Approach
Authors: Dong Yu, Li Deng
Series Title: Signals and Communication Technology
DOI: https://doi.org/10.1007/978-1-4471-5779-3
Publisher: Springer London
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag London 2015
Hardcover ISBN: 978-1-4471-5778-6Published: 28 November 2014
Softcover ISBN: 978-1-4471-6967-3Published: 10 September 2016
eBook ISBN: 978-1-4471-5779-3Published: 11 November 2014
Series ISSN: 1860-4862
Series E-ISSN: 1860-4870
Edition Number: 1
Number of Pages: XXVI, 321
Number of Illustrations: 62 b/w illustrations
Topics: Signal, Image and Speech Processing, Engineering Acoustics, Computer Appl. in Social and Behavioral Sciences