Actions for Automatic Speech Recognition [electronic resource] : A Deep Learning Approach

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Automatic Speech Recognition [electronic resource] : A Deep Learning Approach / by Dong Yu, Li Deng

Author
Yu, Dong
Published
London : Springer London : Imprint: Springer, 2015.
Physical Description
XXVI, 321 pages 62 illustrations : online resource
Additional Creators
Deng, Li. and SpringerLink (Online service)
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Contents
Section 1: Automatic speech recognition: Background -- Feature extraction: basic frontend -- Acoustic model: Gaussian mixture hidden Markov model -- Language model: stochastic N-gram -- Historical reviews of speech recognition research: 1st, 2nd, 3rd, 3.5th, and 4th generations -- Section 2: Advanced feature extraction and transformation -- Unsupervised feature extraction -- Discriminative feature transformation -- Section 3: Advanced acoustic modeling -- Conditional random field (CRF) and hidden conditional random field (HCRF) -- Deep-Structured CRF -- Semi-Markov conditional random field -- Deep stacking models -- Deep neural network – hidden Markov hybrid model -- Section 4: Advanced language modeling -- Discriminative Language model -- Log-linear language model -- Neural network language model.
Summary
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.
Subject(s)
ISBN
9781447157793
Digital File Characteristics
text file PDF
Note
AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
Part Of
Springer eBooks
View MARC record | catkey: 14085371