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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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Title
New Era for Robust Speech Recognition
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-64680-0
ISBNs
978-3-31-964679-4, 978-3-31-964680-0
Editors

Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 21%
Researcher 9 13%
Student > Ph. D. Student 9 13%
Student > Bachelor 7 10%
Lecturer 3 4%
Other 12 18%
Unknown 13 19%
Readers by discipline Count As %
Computer Science 21 31%
Engineering 19 28%
Economics, Econometrics and Finance 2 3%
Decision Sciences 2 3%
Physics and Astronomy 2 3%
Other 3 4%
Unknown 18 27%