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Automatic Speech Recognition: A Deep Learning Approach (Signals and Communication Technology) 🔍
Dong Yu, Li Deng (auth.) Springer-Verlag London, Signals and Communication Technology, Signals and Communication Technology, 1, 2015
英语 [en] · PDF · 7.9MB · 2015 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/scihub/upload/zlib · Save
描述
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.
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lgli/G:\!genesis\_add\!woodhead\Springer\bok%3A978-1-4471-5779-3.pdf
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lgrsnf/G:\!genesis\_add\!woodhead\Springer\bok%3A978-1-4471-5779-3.pdf
备用文件名
nexusstc/Automatic Speech Recognition: A Deep Learning Approach/de2f936b2c7242f06b98cdd7ab823f50.pdf
备用文件名
scihub/10.1007/978-1-4471-5779-3.pdf
备用文件名
zlib/Computers/Artificial Intelligence (AI)/Dong Yu, Li Deng/Automatic Speech Recognition: A Deep Learning Approach_2485780.pdf
备选标题
Automatic Speech Recognition: A Discriminative and Hierarchical Modeling Approach
备选作者
Adobe Acrobat Pro 9.1.0
备选作者
Yu, Dong, Deng, Li
备用出版商
Springer London : Imprint : Springer
备用出版商
Springer London Ltd
备用版本
Signals and communication technology, Aufl. 2015, London, 2015
备用版本
Signals and communication technology, London, 2014
备用版本
United Kingdom and Ireland, United Kingdom
备用版本
2015, 2014-11-28
备用版本
2015, PS, 2014
备用版本
2015, PT, 2014
元数据中的注释
sm33468274
元数据中的注释
producers:
Acrobat Distiller 10.0.0 (Windows)
元数据中的注释
{"container_title":"Signals and Communication Technology","edition":"1","isbns":["1447157788","1447157796","9781447157786","9781447157793"],"issns":["1860-4862","1860-4870"],"last_page":328,"publisher":"Springer London","series":"Signals and Communication Technology"}
备用描述
Front Matter....Pages i-xxvi
Introduction....Pages 1-9
Front Matter....Pages 11-11
Gaussian Mixture Models....Pages 13-21
Hidden Markov Models and the Variants....Pages 23-54
Front Matter....Pages 55-55
Deep Neural Networks....Pages 57-77
Advanced Model Initialization Techniques....Pages 79-95
Front Matter....Pages 97-97
Deep Neural Network-Hidden Markov Model Hybrid Systems....Pages 99-116
Training and Decoding Speedup....Pages 117-136
Deep Neural Network Sequence-Discriminative Training....Pages 137-153
Front Matter....Pages 155-155
Feature Representation Learning in Deep Neural Networks....Pages 157-175
Fuse Deep Neural Network and Gaussian Mixture Model Systems....Pages 177-191
Adaptation of Deep Neural Networks....Pages 193-215
Front Matter....Pages 217-217
Representation Sharing and Transfer in Deep Neural Networks....Pages 219-235
Recurrent Neural Networks and Related Models....Pages 237-266
Computational Network....Pages 267-298
Summary and Future Directions....Pages 299-315
Back Matter....Pages 317-321
备用描述
This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition
备用描述
This book reviews past and present work on discriminative and hierarchical models for both acoustic and language modeling. It also analyzes the research direction and trends towards establishing future-generation speech recognition
备用描述
Signals and Communication Technology
Erscheinungsdatum: 28.11.2014
开源日期
2015-02-17
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