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000724273 019__ $$a900018306$$a908088137
000724273 020__ $$a9781447157793$$qelectronic book
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000724273 050_4 $$aTK7895.S65
000724273 08204 $$a006.4/54$$223
000724273 1001_ $$aYu, Dong,$$d1969-$$eauthor.
000724273 24510 $$aAutomatic speech recognition$$h[electronic resource] :$$ba deep learning approach /$$cDong Yu, Li Deng.
000724273 264_1 $$aLondon :$$bSpringer,$$c[2015]
000724273 264_4 $$c©2015
000724273 300__ $$a1 online resource (xxvi, 321 pages) :$$billustrations.
000724273 336__ $$atext$$btxt$$2rdacontent
000724273 337__ $$acomputer$$bc$$2rdamedia
000724273 338__ $$aonline resource$$bcr$$2rdacarrier
000724273 4901_ $$aSignals and Communication Technology,$$x1860-4862
000724273 504__ $$aIncludes bibliographical references and index.
000724273 5050_ $$aForeword; Preface; Contents; Acronyms; Symbols; 1 Introduction; 1.1 Automatic Speech Recognition: A Bridge for Better Communication; 1.1.1 Human -- Human Communication; 1.1.2 Human -- Machine Communication; 1.2 Basic Architecture of ASR Systems; 1.3 Book Organization; 1.3.1 Part I: Conventional Acoustic Models; 1.3.2 Part II: Deep Neural Networks; 1.3.3 Part III: DNN-HMM Hybrid Systems for ASR; 1.3.4 Part IV: Representation Learning in Deep Neural Networks; 1.3.5 Part V: Advanced Deep Models; References; Part IConventional Acoustic Models; 2 Gaussian Mixture Models; 2.1 Random Variables
000724273 5058_ $$a2.2 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
000724273 5058_ $$a3.4 EM Algorithm and Its Application to Learning HMM Parameters3.4.1 Introduction to EM Algorithm; 3.4.2 Applying EM to Learning the HMM -- Baum-Welch Algorithm; 3.5 Viterbi Algorithm for Decoding HMM State Sequences; 3.5.1 Dynamic Programming and Viterbi Algorithm; 3.5.2 Dynamic Programming for Decoding HMM States; 3.6 The HMM and Variants for Generative Speech Modeling and Recognition; 3.6.1 GMM-HMMs for Speech Modeling and Recognition; 3.6.2 Trajectory and Hidden Dynamic Models for Speech Modeling and Recognition
000724273 5058_ $$a3.6.3 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
000724273 5058_ $$a4.3.10 Reproducibility and RestartabilityReferences; 5 Advanced Model Initialization Techniques; 5.1 Restricted Boltzmann Machines; 5.1.1 Properties of RBMs; 5.1.2 RBM Parameter Learning; 5.2 Deep Belief Network Pretraining; 5.3 Pretraining with Denoising Autoencoder; 5.4 Discriminative Pretraining; 5.5 Hybrid Pretraining; 5.6 Dropout Pretraining; References; Part IIIDeep Neural Network-Hidden MarkovModel Hybrid Systems for AutomaticSpeech Recognition; 6 Deep Neural Network-Hidden Markov Model Hybrid Systems; 6.1 DNN-HMM Hybrid Systems; 6.1.1 Architecture; 6.1.2 Decoding with CD-DNN-HMM
000724273 506__ $$aAccess limited to authorized users.
000724273 520__ $$aThis book reviews past and present work on discriminative and hierarchical models for both acoustic and language modeling. It also analyzes the research direction and trends towards establishing future-generation speech recognition.
000724273 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 8, 2015).
000724273 650_0 $$aAutomatic speech recognition.
000724273 7001_ $$aDeng, Li,$$d1958-$$eauthor.
000724273 77608 $$iPrint version:$$aYu, Dong$$tAutomatic Speech Recognition : A Deep Learning Approach$$dLondon : Springer London,c2014$$z9781447157786
000724273 830_0 $$aSignals and communication technology.
000724273 852__ $$bebk
000724273 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-1-4471-5779-3$$zOnline Access$$91397441.1
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