Automatic speech recognition : a deep learning approach (eBook, 2014) [WorldCat.org]
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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
Summary:
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.

Abstract:

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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