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New Era for Robust Speech Recognition

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Cover of 'New Era for Robust Speech Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Preliminaries
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    Chapter 2 Multichannel Speech Enhancement Approaches to DNN-Based Far-Field Speech Recognition
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    Chapter 3 Multichannel Spatial Clustering Using Model-Based Source Separation
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    Chapter 4 Discriminative Beamforming with Phase-Aware Neural Networks for Speech Enhancement and Recognition
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    Chapter 5 Raw Multichannel Processing Using Deep Neural Networks
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    Chapter 6 Novel Deep Architectures in Speech Processing
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    Chapter 7 Deep Recurrent Networks for Separation and Recognition of Single-Channel Speech in Nonstationary Background Audio
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    Chapter 8 Robust Features in Deep-Learning-Based Speech Recognition
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    Chapter 9 Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognition
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    Chapter 10 Training Data Augmentation and Data Selection
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    Chapter 11 Advanced Recurrent Neural Networks for Automatic Speech Recognition
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    Chapter 12 Sequence-Discriminative Training of Neural Networks
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    Chapter 13 End-to-End Architectures for Speech Recognition
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    Chapter 14 The CHiME Challenges: Robust Speech Recognition in Everyday Environments
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    Chapter 15 The REVERB Challenge: A Benchmark Task for Reverberation-Robust ASR Techniques
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    Chapter 16 Distant Speech Recognition Experiments Using the AMI Corpus
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    Chapter 17 Toolkits for Robust Speech Processing
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    Chapter 18 Speech Research at Google to Enable Universal Speech Interfaces
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    Chapter 19 Challenges in and Solutions to Deep Learning Network Acoustic Modeling in Speech Recognition Products at Microsoft
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    Chapter 20 Advanced ASR Technologies for Mitsubishi Electric Speech Applications
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