{"product_id":"distant-speech-recognition-isbn-9780470517048","title":"Distant Speech Recognition","description":"\u003cb\u003eA complete overview of distant automatic speech recognition\u003c\/b\u003e  \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eDistant Speech Recognition\u003c\/i\u003epresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKey Features:\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCovers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it\u003c\/li\u003e \u003cli\u003eProvides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems\u003c\/li\u003e \u003cli\u003eGives relevant background information in acoustics and filter techniques,\u003c\/li\u003e \u003cli\u003eExplains the extraction and enhancement of classification relevant speech features\u003c\/li\u003e \u003cli\u003eDescribes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques\u003c\/li\u003e \u003cli\u003eDiscusses the use of multi-microphone configurations for speaker tracking and channel combination\u003c\/li\u003e \u003cli\u003ePresents several applications of the methods and technologies described in this book\u003c\/li\u003e \u003cli\u003eAccompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis 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.\u003c\/p\u003e  \u003cb\u003eForeword.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Research and Applications in Academia and Industry.\u003c\/p\u003e \u003cp\u003e1.2 Challenges in Distant Speech Recognition.\u003c\/p\u003e \u003cp\u003e1.3 System Evaluation.\u003c\/p\u003e \u003cp\u003e1.4 Fields of Speech Recognition.\u003c\/p\u003e \u003cp\u003e1.5 Robust Perception.\u003c\/p\u003e \u003cp\u003e1.6 Organizations, Conferences and Journals.\u003c\/p\u003e \u003cp\u003e1.7 Useful Tools, Data Resources and Evaluation Campaigns.\u003c\/p\u003e \u003cp\u003e1.8 Organization of this Book.\u003c\/p\u003e \u003cp\u003e1.9 Principal Symbols used Throughout the Book.\u003c\/p\u003e \u003cp\u003e1.10 Units used Throughout the Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Acoustics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Physical Aspect of Sound.\u003c\/p\u003e \u003cp\u003e2.2 Speech Signals.\u003c\/p\u003e \u003cp\u003e2.3 Human Perception of Sound.\u003c\/p\u003e \u003cp\u003e2.4 The Acoustic Environment.\u003c\/p\u003e \u003cp\u003e2.5 Recording Techniques and Sensor Configuration.\u003c\/p\u003e \u003cp\u003e2.6 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e2.7 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Signal Processing and Filtering Techniques.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Linear Time-Invariant Systems.\u003c\/p\u003e \u003cp\u003e3.2 The Discrete Fourier Transform.\u003c\/p\u003e \u003cp\u003e3.3 Short-Time Fourier Transform.\u003c\/p\u003e \u003cp\u003e3.4 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e3.5 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Bayesian Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Sequential Bayesian Estimation.\u003c\/p\u003e \u003cp\u003e4.2 Wiener Filter.\u003c\/p\u003e \u003cp\u003e4.3 Kalman Filter and Variations.\u003c\/p\u003e \u003cp\u003e4.4 Particle Filters.\u003c\/p\u003e \u003cp\u003e4.5 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e4.6 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Speech Feature Extraction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Short-Time Spectral Analysis.\u003c\/p\u003e \u003cp\u003e5.2 Perceptually Motivated Representation.\u003c\/p\u003e \u003cp\u003e5.3 Spectral Estimation and Analysis.\u003c\/p\u003e \u003cp\u003e5.4 Cepstral Processing.\u003c\/p\u003e \u003cp\u003e5.5 Comparison between Mel Frequency, Perceptual LP and warped MVDR Cepstral Coefficient Frontends.\u003c\/p\u003e \u003cp\u003e5.6 Feature Augmentation.\u003c\/p\u003e \u003cp\u003e5.7 Feature Reduction.\u003c\/p\u003e \u003cp\u003e5.8 Feature-Space Minimum Phone Error.\u003c\/p\u003e \u003cp\u003e5.9 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e5.10 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Speech Feature Enhancement.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Noise and Reverberation in Various Domains.\u003c\/p\u003e \u003cp\u003e6.2 Two Principal Approaches.\u003c\/p\u003e \u003cp\u003e6.3 Direct Speech Feature Enhancement.\u003c\/p\u003e \u003cp\u003e6.4 Schematics of Indirect Speech Feature Enhancement.\u003c\/p\u003e \u003cp\u003e6.5 Estimating Additive Distortion.\u003c\/p\u003e \u003cp\u003e6.6 Estimating Convolutional Distortion.\u003c\/p\u003e \u003cp\u003e6.7 Distortion Evolution.\u003c\/p\u003e \u003cp\u003e6.8 Distortion Evaluation.\u003c\/p\u003e \u003cp\u003e6.9 Distortion Compensation.\u003c\/p\u003e \u003cp\u003e6.10 Joint Estimation of Additive and Convolutional Distortions.\u003c\/p\u003e \u003cp\u003e6.11 Observation Uncertainty.\u003c\/p\u003e \u003cp\u003e6.12 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e6.13 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Search: Finding the Best Word Hypothesis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Fundamentals of Search.\u003c\/p\u003e \u003cp\u003e7.2 Weighted Finite-State Transducers.\u003c\/p\u003e \u003cp\u003e7.3 Knowledge Sources.\u003c\/p\u003e \u003cp\u003e7.4 Fast On-the-Fly Composition.\u003c\/p\u003e \u003cp\u003e7.5 Word and Lattice Combination.\u003c\/p\u003e \u003cp\u003e7.6 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e7.7 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Hidden Markov Model Parameter Estimation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Maximum Likelihood Parameter Estimation.\u003c\/p\u003e \u003cp\u003e8.2 Discriminative Parameter Estimation.\u003c\/p\u003e \u003cp\u003e8.3 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e8.4 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Feature and Model Transformation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Feature Transformation Techniques.\u003c\/p\u003e \u003cp\u003e9.2 Model Transformation Techniques.\u003c\/p\u003e \u003cp\u003e9.3 Acoustic Model Combination.\u003c\/p\u003e \u003cp\u003e9.4 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e9.5 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Speaker Localization and Tracking.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Conventional Techniques.\u003c\/p\u003e \u003cp\u003e10.2 Speaker Tracking with the Kalman Filter.\u003c\/p\u003e \u003cp\u003e10.3 Tracking Multiple Simultaneous Speakers.\u003c\/p\u003e \u003cp\u003e10.4 Audio-Visual Speaker Tracking.\u003c\/p\u003e \u003cp\u003e10.5 Speaker Tracking with the Particle Filter.\u003c\/p\u003e \u003cp\u003e10.6 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e10.7 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Digital Filter Banks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Uniform Discrete Fourier Transform Filter Banks.\u003c\/p\u003e \u003cp\u003e11.2 Polyphase Implementation.\u003c\/p\u003e \u003cp\u003e11.3 Decimation and Expansion.\u003c\/p\u003e \u003cp\u003e11.4 Noble Identities.\u003c\/p\u003e \u003cp\u003e11.5 Nyquist(\u003ci\u003eM\u003c\/i\u003e) Filters.\u003c\/p\u003e \u003cp\u003e11.6 Filter Bank Design of De Haan \u003ci\u003eet al\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003e11.7 Filter Bank Design with the Nyquist(\u003ci\u003eM\u003c\/i\u003e) Criterion.\u003c\/p\u003e \u003cp\u003e11.8 Quality Assessment of Filter Bank Prototypes.\u003c\/p\u003e \u003cp\u003e11.9 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e11.10 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Blind Source Separation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Channel Quality and Selection.\u003c\/p\u003e \u003cp\u003e12.2 Independent Component Analysis.\u003c\/p\u003e \u003cp\u003e12.3 BSS Algorithms based on Second-Order Statistics.\u003c\/p\u003e \u003cp\u003e12.4 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e12.5 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Beamforming.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Beamforming Fundamentals.\u003c\/p\u003e \u003cp\u003e13.2 Beamforming Performance Measures.\u003c\/p\u003e \u003cp\u003e13.3 Conventional Beamforming Algorithms.\u003c\/p\u003e \u003cp\u003e13.4 Recursive Algorithms.\u003c\/p\u003e \u003cp\u003e13.5 Nonconventional Beamforming Algorithms.\u003c\/p\u003e \u003cp\u003e13.6 Array Shape Calibration.\u003c\/p\u003e \u003cp\u003e13.7 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e13.8 Principal Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Hands On.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Example Room Configurations.\u003c\/p\u003e \u003cp\u003e14.2 Automatic Speech Recognition Engines.\u003c\/p\u003e \u003cp\u003e14.3 Word Error Rate.\u003c\/p\u003e \u003cp\u003e14.4 Single-Channel Feature Enhancement Experiments.\u003c\/p\u003e \u003cp\u003e14.5 Acoustic Speaker-Tracking Experiments.\u003c\/p\u003e \u003cp\u003e14.6 Audio-Video Speaker-Tracking Experiments.\u003c\/p\u003e \u003cp\u003e14.7 Speaker-Tracking Performance vs Word Error Rate.\u003c\/p\u003e \u003cp\u003e14.8 Single-Speaker Beamforming Experiments.\u003c\/p\u003e \u003cp\u003e14.9 Speech Separation Experiments.\u003c\/p\u003e \u003cp\u003e14.10 Filter Bank Experiments.\u003c\/p\u003e \u003cp\u003e14.11 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendices.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA List of Abbreviations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Useful Background.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Discrete Cosine Transform.\u003c\/p\u003e \u003cp\u003eB.2 Matrix Inversion Lemma.\u003c\/p\u003e \u003cp\u003eB.3 Cholesky Decomposition.\u003c\/p\u003e \u003cp\u003eB.4 Distance Measures.\u003c\/p\u003e \u003cp\u003eB.5 Super-Gaussian Probability Density Functions.\u003c\/p\u003e \u003cp\u003eB.6 Entropy.\u003c\/p\u003e \u003cp\u003eB.7 Relative Entropy.\u003c\/p\u003e \u003cp\u003eB.8 Transformation Law of Probabilities.\u003c\/p\u003e \u003cp\u003eB.9 Cascade of Warping Stages.\u003c\/p\u003e \u003cp\u003eB.10 Taylor Series.\u003c\/p\u003e \u003cp\u003eB.11 Correlation and Covariance.\u003c\/p\u003e \u003cp\u003eB.12 Bessel Functions.\u003c\/p\u003e \u003cp\u003eB.13 Proof of the Nyquist–Shannon Sampling Theorem.\u003c\/p\u003e \u003cp\u003eB.14 Proof of Equations (11.31–11.32).\u003c\/p\u003e \u003cp\u003eB.15 Givens Rotations.\u003c\/p\u003e \u003cp\u003eB.16 Derivatives with Respect to Complex Vectors.\u003c\/p\u003e \u003cp\u003eB.17 Perpendicular Projection Operators.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e \u003cb\u003eMatthias Wölfel\u003c\/b\u003e 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.  \u003cp\u003e\u003cb\u003eJohn McDonough\u003c\/b\u003e holds a Ph.D. in electrical and computer engineering from the Johns Hopkins Univerity. He has taught the courses \u003ci\u003eMan-Machine Communication\u003c\/i\u003e and \u003ci\u003eMicrophone Arrays: Gateway to Hands Free Automatic Speech Recognition\u003c\/i\u003e 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. \u003c\/p\u003e  \u003cb\u003eA complete overview of distant automatic speech recognition\u003c\/b\u003e  \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eDistant Speech Recognition\u003c\/i\u003epresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKey Features:\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e•Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it\u003c\/p\u003e \u003cp\u003e•Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems\u003c\/p\u003e \u003cp\u003e•Gives relevant background information in acoustics and filter techniques,\u003c\/p\u003e \u003cp\u003e•Explains the extraction and enhancement of classification relevant speech features\u003c\/p\u003e \u003cp\u003e•Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques\u003c\/p\u003e \u003cp\u003e•Discusses the use of multi-microphone configurations for speaker tracking and channel combination\u003c\/p\u003e \u003cp\u003e•Presents several applications of the methods and technologies described in this book\u003c\/p\u003e \u003cp\u003e•Accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989076951269,"sku":"NP9780470517048","price":139.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470517048.jpg?v=1761782698","url":"https:\/\/k12savings.com\/products\/distant-speech-recognition-isbn-9780470517048","provider":"K12savings","version":"1.0","type":"link"}