Distant Speech Recognition

By Matthias Wölfel, J. McDonough. 2009

337 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b5444b7c1ba6a57564dff83358a747b3bd20c618

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Citations in the corpus

Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, background): “forcing the user to speak very close to a microphone-equipped device. There are, however, various reallife situations where Distant Speech Recognition (DSR) is more natural, convenient and attractive [1]. An emerging application is, for instance, speech-based domestic control, where users might pref”
An Overview of Noise-Robust Automatic Speech Recognition (2014, background): “Although still a digit recognition task, the utterances in Aurora 3 are collected in real noisy environments.”
Speaker Diarization: A Review of Recent Research (2010, neutral): “Among alternative beamforming algorithms we ﬁnd maximum likelihood (ML) [46] or generalized sidelobe canceller (GSC) [47] which adaptively ﬁnd the optimum parameters, and minimum variance distortionless response (MVDR) [48] when prior information on ambient noise is available.”
The reverb challenge: A common evaluation framework for dereverberation and recognition of reverberant speech (2013, background): “Although a range of signal processing and speech recognition techniques are available for combating the effect of additive noise [1, 2], finding practical algorithms that can reduce the detrimental effect of reverberation has been one of the toughest challenges in the field of distant-speech enhance”
The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, background): “There are indeed various real-life situations where a distant-talking (far-ﬁeld) 1 interac-1 tion is more natural, convenient and attractive [2].”
An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, neutral): “Speech enhancement and automatic speech recognition (ASR) in the presence of reverberation and nonstationary noise are still challenging tasks today (Baker et al., 2009; W¨olfel and McDonough, 2009; Virtanen et al., 2012; Li et al., 2015).”
The Sweet-Home speech and multimodal corpus for home automation interaction (2014, background): “The French Home Automation Speech subset was used for studies related to vocal order recognition in Distant Speech conditions (Vacher et al., 2012).”
A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research (2016, background): “jp 1NTT Communication Science Laboratories, Kyoto, Japan Full list of author information is available at the end of the article which severely degrade the audible speech quality of the captured signals [1] and the performance of automatic speech recognition (ASR) [2, 3].”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, neutral): “For the use of speech enhancement and musical instrument separation as pre-processing steps for speech recognition and music information retrieval, see, e.g., [28]–[31].”
VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, neutral): “While this remains the most frequent use case today, speaker localization, speech enhancement, and ASR in distant-microphone scenarios remain challenging (Baker et al., 2009; Wölfel and McDonough, 2009; Cohen et al., 2010; Virtanen et al., 2012; Li et al., 2015; Vincent et al., 2018).”
On the selection of the impulse responses for distant-speech recognition based on contaminated speech training (2014, background): “However, in the case of distant-speech interaction, the state-of-the-art ASR technologies still exhibit a signiﬁcant lack of robustness and ﬂexibility and, as a result, an unacceptable variability in their performance [3].”
Using neural network front-ends on far field multiple microphones based speech recognition (2014, background): “Index Terms— speech recognition, multiple microphone, beamforming, deep neural networks”
Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016, background): “The latter case is called distant or far-field ASR and shows a significant drop in performance, which is mainly due to three different types of distortion [19]: (a) background noise, (b) echo and reverberation, and (c) other types of distortions, e.”
Hybrid acoustic models for distant and multichannel large vocabulary speech recognition (2013, background): “Index Terms— Distant Speech Recognition, Deep Neural Networks, Microphone Arrays, Beamforming, Meeting recognition 1.”