book

Robust Automatic Speech Recognition

by Jinyu Li, Li Deng, Reinhold Haeb-Umbach, Yifan Gong

October 2015

Intermediate to advanced

306 pages

10h 38m

English

Academic Press

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Abstract1.1 Automatic Speech Recognition1.2 Robustness to Noisy Environments1.3 Existing Surveys in the Area1.4 Book Structure Overview

Abstract2.1 Introduction: Components of Speech Recognition2.2 Gaussian Mixture Models2.3 Hidden Markov Models and the Variants2.4 Deep Learning and Deep Neural Networks2.5 Summary
Abstract3.1 Standard Evaluation Databases3.2 Modeling Distortions of Speech in Acoustic Environments3.3 Impact of Acoustic Distortion on Gaussian Modeling3.4 Impact of Acoustic Distortion on DNN Modeling3.5 A General Framework for Robust Speech Recognition3.6 Categorizing Robust ASR Techniques: An Overview3.7 Summary
Abstract4.1 Feature-Space Approaches4.2 Model-Space Approaches4.3 Summary
Abstract5.1 Learning from Stereo Data5.2 Learning from Multi-Environment Data5.3 Summary
Abstract6.1 Parallel Model Combination6.2 Vector Taylor Series6.3 Sampling-Based Methods6.4 Acoustic Factorization6.5 Summary
Abstract7.1 Model-Domain Uncertainty7.2 Feature-Domain Uncertainty7.3 Joint Uncertainty Decoding7.4 Missing-Feature Approaches7.5 Summary
Abstract8.1 Speaker Adaptive and Source Normalization Training8.2 Model Space Noise Adaptive Training8.3 Joint Training for DNN8.4 Summary
Abstract9.1 Introduction9.2 Acoustic Impulse Response9.3 A Model of Reverberated Speech in Different Domains9.4 The Effect of Reverberation on ASR Performance9.5 Linear Filtering Approaches9.6 Magnitude or Power Spectrum Enhancement9.7 Feature Domain Approaches9.8 Acoustic Model Domain Approaches9.9 The REVERB Challenge9.10 To Probe Further9.11 Summary
Abstract10.1 Introduction10.2 The Acoustic Beamforming Problem10.3 Fundamentals of Data-Dependent Beamforming10.4 Multi-Channel Speech Recognition10.5 To Probe Further10.6 Summary
Abstract11.1 Robust Methods in the Era of GMM11.2 Robust Methods in the Era of DNN11.3 Multi-Channel Input and Robustness to Reverberation11.4 Epilogue

Content preview from Robust Automatic Speech Recognition

Notations

Mathematical language is an essential tool in this book. We thus introduce our mathematical notations right from the start in the following table, separated in five general categories. Throughout this book, both matrices and vectors are in bold type, and matrices are capitalized.

Definitions of a Subset of Commonly Used Symbols and Notations, Grouped in Five Separate General Categories

General notation
≈	approximately equal to
$\propto$	proportional to
s	scalar quantity (lowercase plain letter)
v	vector quantity (lowercase bold letter)
v_i	the ith element of vector v
M	matrix (uppercase bold letter)
m_ij	the (i,j)th element of the matrix M
M^T	transpose of ...

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Publisher Resources

ISBN: 9780128026168