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Distant Speech Recognition
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Advertising Age Encyclopedia of Advertising
John McDonough, Karen Egolf
For a full list of entries and contributors, a generous selection of sample entries, and more, visit the The "e;Advertising Age"e; Encyclopedia of Advertising website.Featuring nearly 600 extensively illustrated entries, The Advertising Ag...
Bloggat om Distant Speech Recognition
Matthias Woelfel 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.
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