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Automatic speech recognition : a deep learning approach

Author: Dong Yu; Li Deng
Publisher: London : Springer, [2015] ©2015
Series: Signals and communication technology.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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.

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Genre/Form: Llibres electrònics
Additional Physical Format: Print version:
Yu, Dong.
Automatic Speech Recognition : A Deep Learning Approach.
London : Springer London, ©2014
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Dong Yu; Li Deng
ISBN: 9781447157793 1447157796 1447157788 9781447157786
OCLC Number: 895161787
Description: 1 online resource (xxvi, 321 pages) : illustrations
Contents: Foreword; Preface; Contents; Acronyms; Symbols; 1 Introduction; 1.1 Automatic Speech Recognition: A Bridge for Better Communication; 1.1.1 Human --
Human Communication; 1.1.2 Human --
Machine Communication; 1.2 Basic Architecture of ASR Systems; 1.3 Book Organization; 1.3.1 Part I: Conventional Acoustic Models; 1.3.2 Part II: Deep Neural Networks; 1.3.3 Part III: DNN-HMM Hybrid Systems for ASR; 1.3.4 Part IV: Representation Learning in Deep Neural Networks; 1.3.5 Part V: Advanced Deep Models; References; Part IConventional Acoustic Models; 2 Gaussian Mixture Models; 2.1 Random Variables. 2.2 Gaussian and Gaussian-Mixture Random Variables2.3 Parameter Estimation; 2.4 Mixture of Gaussians as a Model for the Distribution of Speech Features; References; 3 Hidden Markov Models and the Variants; 3.1 Introduction; 3.2 Markov Chains; 3.3 Hidden Markov Sequences and Models; 3.3.1 Characterization of a Hidden Markov Model; 3.3.2 Simulation of a Hidden Markov Model; 3.3.3 Likelihood Evaluation of a Hidden Markov Model; 3.3.4 An Algorithm for Efficient Likelihood Evaluation; 3.3.5 Proofs of the Forward and Backward Recursions. 3.4 EM Algorithm and Its Application to Learning HMM Parameters3.4.1 Introduction to EM Algorithm; 3.4.2 Applying EM to Learning the HMM --
Baum-Welch Algorithm; 3.5 Viterbi Algorithm for Decoding HMM State Sequences; 3.5.1 Dynamic Programming and Viterbi Algorithm; 3.5.2 Dynamic Programming for Decoding HMM States; 3.6 The HMM and Variants for Generative Speech Modeling and Recognition; 3.6.1 GMM-HMMs for Speech Modeling and Recognition; 3.6.2 Trajectory and Hidden Dynamic Models for Speech Modeling and Recognition. 3.6.3 The Speech Recognition Problem Using Generative Models of HMM and Its VariantsReferences; Part IIDeep Neural Networks; 4 Deep Neural Networks; 4.1 The Deep Neural Network Architecture ; 4.2 Parameter Estimation with Error Backpropagation; 4.2.1 Training Criteria; 4.2.2 Training Algorithms; 4.3 Practical Considerations ; 4.3.1 Data Preprocessing ; 4.3.2 Model Initialization; 4.3.3 Weight Decay; 4.3.4 Dropout; 4.3.5 Batch Size Selection; 4.3.6 Sample Randomization; 4.3.7 Momentum; 4.3.8 Learning Rate and Stopping Criterion; 4.3.9 Network Architecture. 4.3.10 Reproducibility and RestartabilityReferences; 5 Advanced Model Initialization Techniques; 5.1 Restricted Boltzmann Machines; 5.1.1 Properties of RBMs; 5.1.2 RBM Parameter Learning; 5.2 Deep Belief Network Pretraining; 5.3 Pretraining with Denoising Autoencoder; 5.4 Discriminative Pretraining; 5.5 Hybrid Pretraining; 5.6 Dropout Pretraining; References; Part IIIDeep Neural Network-Hidden MarkovModel Hybrid Systems for AutomaticSpeech Recognition; 6 Deep Neural Network-Hidden Markov Model Hybrid Systems; 6.1 DNN-HMM Hybrid Systems; 6.1.1 Architecture; 6.1.2 Decoding with CD-DNN-HMM.
Series Title: Signals and communication technology.
Responsibility: Dong Yu, Li Deng.


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  Read more...


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"Deep Learning (DL) has demonstrated a phenomenal success in various AI applications. ... This book by two leading experts in Deep Learning is certainly a welcome addition to the literature of the Read more...

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