Automatic Speech Recognition
A Deep Learning Approach
Authors: Yu, Dong, Deng, Li
- 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
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- About this book
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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
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“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)
- Download Preface 1 PDF (52.1 KB)
- Download Sample pages 2 PDF (116 KB)
- Download Table of contents PDF (236.6 KB)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Automatic Speech Recognition
- Book Subtitle
- A Deep Learning Approach
- Authors
-
- Dong Yu
- Li Deng
- Series Title
- Signals and Communication Technology
- Copyright
- 2015
- Publisher
- Springer-Verlag London
- Copyright Holder
- Springer-Verlag London
- eBook ISBN
- 978-1-4471-5779-3
- DOI
- 10.1007/978-1-4471-5779-3
- Hardcover ISBN
- 978-1-4471-5778-6
- Softcover ISBN
- 978-1-4471-6967-3
- Series ISSN
- 1860-4862
- Edition Number
- 1
- Number of Pages
- XXVI, 321
- Number of Illustrations and Tables
- 62 b/w illustrations
- Topics
