Automatic Speech Recognition: A Deep Learning Approach

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Springer, Nov 11, 2014 - Technology & Engineering - 321 pages
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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.

 

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Contents

1 Introduction
1
Part IConventional Acoustic Models
10
2 Gaussian Mixture Models
11
3 Hidden Markov Models and the Variants
23
Part IIDeep Neural Networks
55
4 Deep Neural Networks
56
5 Advanced Model Initialization Techniques
79
Part IIIDeep Neural NetworkHidden MarkovModel Hybrid Systems for AutomaticSpeech Recognition
96
Part IVRepresentation Learningin Deep Neural Networks
154
9 Feature Representation Learning in Deep Neural Networks
157
10 Fuse Deep Neural Network and Gaussian Mixture Model Systems
176
11 Adaptation of Deep Neural Networks
193
Part VAdvanced Deep Models
216
12 Representation Sharing and Transfer in Deep Neural Networks
219
13 Recurrent Neural Networks and Related Models
236
14 Computational Network
267

6 Deep Neural NetworkHidden Markov Model Hybrid Systems
99
7 Training and Decoding Speedup
117
8 Deep Neural Network SequenceDiscriminative Training
137
15 Summary and Future Directions
299
Index
317
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