Matthias Chr. Woelfel, John McDonough

Distant Speech Recognition

• Gebundenes Buch

Produktbeschreibung

In order to make speech recognition the universally preferred mode of interacting with machines, far-field microphones must attain the same level of performance that is currently available with close-talking microphones. Presenting an up-to-date and comprehensive description of both the theoretic abstraction and practical issues inherent in the distant ASR, Distant Speech Recognition covers the entire distant ASR subject in one volume. Here, researchers, developers, advanced students, professors, and engineers will discover techniques developed in other areas of signal processing that mitigate the deleterious effects of noise and reverberation.

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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.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 594
• Erscheinungstermin: 1. Juni 2009
• Englisch
• Abmessung: 250mm x 176mm x 41mm
• Gewicht: 1144g
• ISBN-13: 9780470517048
• ISBN-10: 0470517042
• Artikelnr.: 25626745

Autorenporträt

Matthias Wölfel will finish his Ph.D on far-field speech recognition in the middle of 2007. He has given seminars in Speech and Robust Speech Recognition and has published more than 25 papers in this field. He has been involved in two European research projects on distant speech recognition: FAME and CHIL. He has written extensive sections of source code of speech enhancement, spectral estimation and feature extraction for automatic recognition, and has participated in NIST Rich Transcription evaluations with focus on far-field speech. John McDonough holds a Ph.D. in electrical and computer engineering from the Johns Hopkins Univerity. He has taught the courses Man-Machine Communication and Microphone Arrays: Gateway to Hands Free Automatic Speech Recognition at the University of Karlsruhe for five years. He has published dozens of conference and journal articles, and written complete software toolkits for source localization, beamforming and automatic speech recognition.

Inhaltsangabe

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.