Distant speech recognition / Matthias Wolfel and John McDonough.

Format:

Book

Author/Creator:

Wölfel, Matthias.

Contributor:

McDonough, John (John W.)

Language:

English

Subjects (All):

Automatic speech recognition.

Pattern perception.

Physical Description:

1 online resource (595 p.)

Edition:

1st ed.

Place of Publication:

Chichester, West Sussex, U.K. : Wiley, c2009.

Language Note:

English

Summary:

A complete overview of distant automatic speech recognition The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed in other areas of signal processing that can mitigate the deleterious effects of noise and reverberation, as well as separating speech from overlapping speakers. Distant Speech Recognitionpresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem. Key Features: *Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it *Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems *Gives relevant background information in acoustics and filter techniques, *Explains the extraction and enhancement of classification relevant speech features *Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques *Discusses the use of multi-microphone configurations for speaker tracking and channel combination *Presents several applications of the methods and technologies described in this book *Accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems This reference will be an invaluable resource for researchers, developers, engineers and other professionals, as well as advanced students in speech technology, signal processing, acoustics, statistics and artificial intelligence fields.

Contents:

Foreword

Preface

1 Introduction

1.1 Research and Applications in Academia and Industry

1.2 Challenges in Distant Speech Recognition

1.3 System Evaluation

1.4 Fields of Speech Recognition

1.5 Robust Perception

1.6 Organizations, Conferences and Journals

1.7 Useful Tools, Data Resources and Evaluation Campaigns

1.8 Organization of this Book

1.9 Principal Symbols used Throughout the Book

1.10 Units used Throughout the Book

2 Acoustics

2.1 Physical Aspect of Sound

2.2 Speech Signals

2.3 Human Perception of Sound

2.4 The Acoustic Environment

2.5 Recording Techniques and Sensor Configuration

2.6 Summary and Further Reading

2.7 Principal Symbols

3 Signal Processing and Filtering Techniques

3.1 Linear Time-Invariant Systems

3.2 The Discrete Fourier Transform

3.3 Short-Time Fourier Transform

3.4 Summary and Further Reading

3.5 Principal Symbols

4 Bayesian Filters

4.1 Sequential Bayesian Estimation

4.2 Wiener Filter

4.3 Kalman Filter and Variations

4.4 Particle Filters

4.5 Summary and Further Reading

4.6 Principal Symbols

5 Speech Feature Extraction

5.1 Short-Time Spectral Analysis

5.2 Perceptually Motivated Representation

5.3 Spectral Estimation and Analysis

5.4 Cepstral Processing

5.5 Comparison between Mel Frequency, Perceptual LP and warped MVDR Cepstral Coefficient Frontends

5.6 Feature Augmentation

5.7 Feature Reduction

5.8 Feature-Space Minimum Phone Error

5.9 Summary and Further Reading

5.10 Principal Symbols

6 Speech Feature Enhancement

6.1 Noise and Reverberation in Various Domains

6.2 Two Principal Approaches

6.3 Direct Speech Feature Enhancement

6.4 Schematics of Indirect Speech Feature Enhancement

6.5 Estimating Additive Distortion

6.6 Estimating Convolutional Distortion

6.7 Distortion Evolution

6.8 Distortion Evaluation

6.9 Distortion Compensation

6.10 Joint Estimation of Additive and Convolutional Distortions.

6.11 Observation Uncertainty

6.12 Summary and Further Reading

6.13 Principal Symbols

7 Search: Finding the Best Word Hypothesis

7.1 Fundamentals of Search

7.2 Weighted Finite-State Transducers

7.3 Knowledge Sources

7.4 Fast On-the-Fly Composition

7.5 Word and Lattice Combination

7.6 Summary and Further Reading

7.7 Principal Symbols

8 Hidden Markov Model Parameter Estimation

8.1 Maximum Likelihood Parameter Estimation

8.2 Discriminative Parameter Estimation

8.3 Summary and Further Reading

8.4 Principal Symbols

9 Feature and Model Transformation

9.1 Feature Transformation Techniques

9.2 Model Transformation Techniques

9.3 Acoustic Model Combination

9.4 Summary and Further Reading

9.5 Principal Symbols

10 Speaker Localization and Tracking

10.1 Conventional Techniques

10.2 Speaker Tracking with the Kalman Filter

10.3 Tracking Multiple Simultaneous Speakers

10.4 Audio-Visual Speaker Tracking

10.5 Speaker Tracking with the Particle Filter

10.6 Summary and Further Reading

10.7 Principal Symbols

11 Digital Filter Banks

11.1 Uniform Discrete Fourier Transform Filter Banks

11.2 Polyphase Implementation

11.3 Decimation and Expansion

11.4 Noble Identities

11.5 Nyquist(M) Filters

11.6 Filter Bank Design of De Haan et al

11.7 Filter Bank Design with the Nyquist(M) Criterion

11.8 Quality Assessment of Filter Bank Prototypes

11.9 Summary and Further Reading

11.10 Principal Symbols

12 Blind Source Separation

12.1 Channel Quality and Selection

12.2 Independent Component Analysis

12.3 BSS Algorithms based on Second-Order Statistics

12.4 Summary and Further Reading

12.5 Principal Symbols

13 Beamforming

13.1 Beamforming Fundamentals

13.2 Beamforming Performance Measures

13.3 Conventional Beamforming Algorithms

13.4 Recursive Algorithms

13.5 Nonconventional Beamforming Algorithms

13.6 Array Shape Calibration

13.7 Summary and Further Reading.

13.8 Principal Symbols

14 Hands On

14.1 Example Room Configurations

14.2 Automatic Speech Recognition Engines

14.3 Word Error Rate

14.4 Single-Channel Feature Enhancement Experiments

14.5 Acoustic Speaker-Tracking Experiments

14.6 Audio-Video Speaker-Tracking Experiments

14.7 Speaker-Tracking Performance vs Word Error Rate

14.8 Single-Speaker Beamforming Experiments

14.9 Speech Separation Experiments

14.10 Filter Bank Experiments

14.11 Summary and Further Reading

Appendices

A List of Abbreviations

B Useful Background

B.1 Discrete Cosine Transform

B.2 Matrix Inversion Lemma

B.3 Cholesky Decomposition

B.4 Distance Measures

B.5 Super-Gaussian Probability Density Functions

B.6 Entropy

B.7 Relative Entropy

B.8 Transformation Law of Probabilities

B.9 Cascade of Warping Stages

B.10 Taylor Series

B.11 Correlation and Covariance

B.12 Bessel Functions

B.13 Proof of the Nyquist / Shannon Sampling Theorem

B.14 Proof of Equations (11.31 / 11.32)

B.15 Givens Rotations

B.16 Derivatives with Respect to Complex Vectors

B.17 Perpendicular Projection Operators

Bibliography

Index.

Notes:

Description based upon print version of record.

Description based on PDF viewed 10/24/2017.

Includes bibliographical references and index.

ISBN:

9786612123047

9781282123045

1282123041

9780470714089

0470714085

9780470714072

0470714077

OCLC:

352829722