Speech Enhancement Paradigm
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[1] Satoshi Takahashi,et al. Jacobian approach to fast acoustic model adaptation , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[2] Marilyn Y. Chen,et al. Acoustic correlates of English and French nasalized vowels. , 1997, The Journal of the Acoustical Society of America.
[3] Neri Merhav,et al. Lower and upper bounds on the minimum mean-square error in composite source signal estimation , 1991, IEEE Trans. Inf. Theory.
[4] Alejandro Correa,et al. Genetic Algorithm Optimization for Selecting the Best Architecture of a Multi-Layer Perceptron Neural Network: A Credit Scoring Case , 2011 .
[5] Kiyohiro Shikano,et al. Musical noise generation analysis for noise reduction methods based on spectral subtraction and MMSE STSA estimation , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[6] Gerhard Rigoll,et al. Maximum mutual information neural networks for hybrid connectionist-HMM speech recognition systems , 1994, IEEE Trans. Speech Audio Process..
[7] Peter Vamplew,et al. Accelerating Real-Valued Genetic Algorithms Using Mutation-with-Momentum , 2005, Australian Conference on Artificial Intelligence.
[8] Mark J. F. Gales,et al. Cepstral parameter compensation for HMM recognition in noise , 1993, Speech Commun..
[9] James E. Baker,et al. Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.
[10] Yi Hu,et al. Subjective comparison and evaluation of speech enhancement algorithms , 2007, Speech Commun..
[11] George Carayannis,et al. Speech enhancement from noise: A regenerative approach , 1991, Speech Commun..
[12] Oliver Lemon,et al. Mixture Model POMDPs for Efficient Handling of Uncertainty in Dialogue Management , 2008, ACL.
[13] Ching-Ta Lu,et al. Enhancement of single channel speech using perceptual-decision-directed approach , 2011, Speech Commun..
[14] Anupam Shukla,et al. Real Life Applications of Soft Computing , 2010 .
[15] Chafic Mokbel,et al. Online adaptation of HMMs to real-life conditions: a unified framework , 2001, IEEE Trans. Speech Audio Process..
[16] Mark J. F. Gales,et al. Adaptive training using discriminative mapping transforms , 2008, INTERSPEECH.
[17] Hynek Hermansky,et al. RASTA processing of speech , 1994, IEEE Trans. Speech Audio Process..
[18] Biing-Hwang Juang,et al. Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.
[19] David Malah,et al. Speech enhancement using a minimum mean-square error log-spectral amplitude estimator , 1984, IEEE Trans. Acoust. Speech Signal Process..
[20] Steve J. Young,et al. Partially observable Markov decision processes for spoken dialog systems , 2007, Comput. Speech Lang..
[21] Weihua Li,et al. Recursive PCA for adaptive process monitoring , 1999 .
[22] Andrew Sekey,et al. An Objective Measure for Predicting Subjective Quality of Speech Coders , 1992, IEEE J. Sel. Areas Commun..
[23] Philipos C. Loizou,et al. A noise-estimation algorithm for highly non-stationary environments , 2006, Speech Commun..
[24] Lotfi A. Zadeh,et al. Fuzzy logic, neural networks, and soft computing , 1993, CACM.
[25] Alexander H. Waibel,et al. Tight coupling of speech recognition and dialog management - dialog-context dependent grammar weighting for speech recognition , 2004, INTERSPEECH.
[26] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[27] Jungyun Seo,et al. Dialogue Strategies to Overcome Speech Recognition Errors in Form-Filling Dialogue , 2009, ICCPOL.
[28] Wonho Yang,et al. A modified bark spectral distortion measure which uses noise masking threshold , 1997, 1997 IEEE Workshop on Speech Coding for Telecommunications Proceedings. Back to Basics: Attacking Fundamental Problems in Speech Coding.
[29] Douglas D. O'Shaughnessy,et al. Real-life speech-enabled system to enhance interaction with rfid networks in noisy environments , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[30] H. Franco,et al. Unsupervised noise model estimation for model-based robust speech recognition , 2003, 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721).
[31] Jyh-Shing Roger Jang,et al. Minimum phone error discriminative training for Mandarin Chinese speaker adaptation , 2008, INTERSPEECH.
[32] Douglas D. O'Shaughnessy,et al. Speech communication : human and machine , 1987 .
[33] Thomas Niesler,et al. Characterisation and simulation of telephone channels using the TIMIT and NTIMIT databases , 2009 .
[34] Wayne H. Ward,et al. Dialog-context dependent language modeling combining n-grams and stochastic context-free grammars , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[35] Milica Gasic,et al. The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management , 2010, Comput. Speech Lang..
[36] Yariv Ephraim,et al. A signal subspace approach for speech enhancement , 1995, IEEE Trans. Speech Audio Process..
[37] Dennis H. Klatt,et al. Prediction of perceived phonetic distance from critical-band spectra: A first step , 1982, ICASSP.
[38] Rainer Martin,et al. Noise power spectral density estimation based on optimal smoothing and minimum statistics , 2001, IEEE Trans. Speech Audio Process..
[39] Eric John Diethorn. Subband noise reduction methods for speech enhancement , 2000 .
[40] Israel Cohen,et al. Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging , 2003, IEEE Trans. Speech Audio Process..
[41] Jukka Saarinen,et al. MLP network for enhancement of noisy MFCC vectors , 1999, EUROSPEECH.
[42] Mark J. F. Gales,et al. Unsupervised Adaptation With Discriminative Mapping Transforms , 2009, IEEE Transactions on Audio, Speech, and Language Processing.
[43] Roberto Pieraccini,et al. Automating spoken dialogue management design using machine learning: An industry perspective , 2008, Speech Commun..
[44] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[45] John H. L. Hansen,et al. Environment mismatch compensation using average eigenspace for speech recognition , 2008, INTERSPEECH.
[46] Rainer Martin,et al. Spectral Subtraction Based on Minimum Statistics , 2001 .
[47] Yifan Gong,et al. Speech recognition in noisy environments: A survey , 1995, Speech Commun..
[48] Lan Wang,et al. MPE-based discriminative linear transform for speaker adaptation , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[49] Saeed Gazor,et al. An adaptive KLT approach for speech enhancement , 2001, IEEE Trans. Speech Audio Process..
[50] Joseph Picone,et al. Signal modeling techniques in speech recognition , 1993, Proc. IEEE.
[51] Frederick Jelinek,et al. Statistical methods for speech recognition , 1997 .
[52] Philip C. Woodland,et al. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models , 1995, Comput. Speech Lang..
[53] David Maxwell Chickering,et al. Improving command and control speech recognition on mobile devices: using predictive user models for language modeling , 2006, User Modeling and User-Adapted Interaction.
[54] Richard M. Stern,et al. Sources of degradation of speech recognition in the telephone network , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.
[55] Jacob Benesty,et al. Springer handbook of speech processing , 2007, Springer Handbooks.
[56] Sara H. Basson,et al. NTIMIT: a phonetically balanced, continuous speech, telephone bandwidth speech database , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[57] H Hermansky,et al. Perceptual linear predictive (PLP) analysis of speech. , 1990, The Journal of the Acoustical Society of America.
[58] M. Halle,et al. Preliminaries to Speech Analysis: The Distinctive Features and Their Correlates , 1961 .
[59] David Pearce,et al. The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions , 2000, INTERSPEECH.
[60] Douglas D. O'Shaughnessy,et al. Speech enhancement using PCA and variance of the reconstruction error in distributed speech recognition , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).
[61] A.V. Oppenheim,et al. Enhancement and bandwidth compression of noisy speech , 1979, Proceedings of the IEEE.
[62] Yongjoo Jung. Improving Robustness in Jacobian Adaptation for Noisy Speech Recognition , 2008, PIT.
[63] James A. Cadzow,et al. Signal enhancement-a composite property mapping algorithm , 1988, IEEE Trans. Acoust. Speech Signal Process..
[64] Milica Gasic,et al. Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager , 2011, TSLP.
[65] Sofia Ben Jebara,et al. Perceptual musical noise reduction using critical bands tonality coefficients and masking thresholds , 2007, INTERSPEECH.
[66] Véronique Delvaux,et al. French nasal vowels: acoustic and articulatory properties , 2002, INTERSPEECH.
[67] Benoît Champagne,et al. Incorporating the human hearing properties in the signal subspace approach for speech enhancement , 2003, IEEE Trans. Speech Audio Process..
[68] Irina Illina,et al. Using genetic algorithms for rapid speaker adaptation , 2003, INTERSPEECH.
[69] Douglas D. O'Shaughnessy,et al. Robust automatic speech recognition in low-SNR car environments by the application of a connectionist subspace-based approach to the melbased cepstral coefficients , 2001, INTERSPEECH.
[70] Schuyler Quackenbush,et al. Objective measures of speech quality , 1995 .
[71] I. Cohen,et al. Noise estimation by minima controlled recursive averaging for robust speech enhancement , 2002, IEEE Signal Processing Letters.
[72] Gary Geunbae Lee,et al. A Frame-Based Probabilistic Framework for Spoken Dialog Management Using Dialog Examples , 2008, SIGDIAL Workshop.
[73] Douglas D. O'Shaughnessy,et al. Auditory-based acoustic distinctive features and spectral cues for automatic speech recognition using a multi-stream paradigm , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[74] S. N. Sivanandam,et al. Introduction to genetic algorithms , 2007 .
[75] Pedro M. Valero-Mora,et al. Determining the Number of Factors to Retain in EFA: An easy-to-use computer program for carrying out Parallel Analysis , 2007 .
[76] Christopher R. Houck,et al. A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .
[77] Nostrand Reinhold,et al. the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .
[78] Katarina Bartkova,et al. Multiple models for improved speech recognition for non-native speakers , 2004 .
[79] A. E. Eiben,et al. Introduction to Evolutionary Computing , 2003, Natural Computing Series.
[80] Louis ten Bosch,et al. Hybrid HMM/BLSTM-RNN for Robust Speech Recognition , 2010, TSD.
[81] Karthik Visweswariah,et al. Language models conditioned on dialog state , 2001, INTERSPEECH.
[82] Wonyong Sung,et al. A voice activity detector employing soft decision based noise spectrum adaptation , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[83] Robert E. Yantorno,et al. Performance of the modified Bark spectral distortion as an objective speech quality measure , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[84] H.B.D. Sorensen,et al. A cepstral noise reduction multi-layer neural network , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[85] Climent Nadeu,et al. A comparative study of parameters and distances for noisy speech recognition , 1991, EUROSPEECH.
[86] Yoh'ichi Tohkura,et al. A weighted cepstral distance measure for speech recognition , 1987, IEEE Trans. Acoust. Speech Signal Process..
[87] Philipos C. Loizou,et al. Speech Enhancement: Theory and Practice , 2007 .
[88] Biing-Hwang Juang,et al. A family of distortion measures based upon projection operation for robust speech recognition , 1989, IEEE Trans. Acoust. Speech Signal Process..
[89] Douglas D. O'Shaughnessy,et al. Robustness of speech recognition using genetic algorithms and a Mel-cepstral subspace approach , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[90] Bernard Widrow,et al. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[91] Jean-Luc Gauvain,et al. Speaker adaptation based on MAP estimation of HMM parameters , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[92] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[93] Douglas D. O'Shaughnessy,et al. A hybrid HMM/autoregressive Time-Delay Neural Network Automatic Speech Recognition system , 2002, 2002 11th European Signal Processing Conference.
[94] Jacob Benesty,et al. Speech Enhancement , 2010 .
[95] S. Joe Qin,et al. Determining the number of principal components for best reconstruction , 1998 .
[96] R. Kumaresan,et al. Data adaptive signal estimation by singular value decomposition of a data matrix , 1982, Proceedings of the IEEE.
[97] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[98] Nadia Nedjah,et al. Evolutionary Computation: from Genetic Algorithms to Genetic Programming , 2006, Genetic Systems Programming.
[99] Jean Caelen. Space/time data-information in the A.R.I.A.L. project ear model , 1985, Speech Commun..
[100] R. R. Leighton,et al. The autoregressive backpropagation algorithm , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[101] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[102] Sid-Ahmed Selouani. “Well Adjusted”: Using Robust and Flexible Speech Recognition Capabilities in Clean to Noisy Mobile Environments , 2010 .
[103] Peder A. Olsen,et al. Dynamic Noise Adaptation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[104] Douglas D. O'Shaughnessy,et al. Speech enhancement using PCA and variance of the reconstruction error model identification , 2007, INTERSPEECH.
[105] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.
[106] Stan Davis,et al. Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .
[107] S. Singh,et al. Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System , 2011, J. Artif. Intell. Res..
[108] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[109] Tanja Schultz,et al. Comparison of acoustic model adaptation techniques on non-native speech , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[110] S. Boll,et al. Suppression of acoustic noise in speech using spectral subtraction , 1979 .
[111] L. Rabiner,et al. An interpretation of the log likelihood ratio as a measure of waveform coder performance , 1980 .
[112] A. Spalanzani,et al. Evolutionary Algorithms for optimizing speech data projection , 1999 .
[113] Hervé Bourlard,et al. Connectionist Speech Recognition: A Hybrid Approach , 1993 .
[114] Roger K. Moore. Computer Speech and Language , 1986 .
[115] Jonathan G. Fiscus,et al. A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER) , 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.
[116] Virginia Teller. Review of Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition by Daniel Jurafsky and James H. Martin. Prentice Hall 2000. , 2000 .
[117] John H. L. Hansen,et al. Environmental Sniffing: Noise Knowledge Estimation for Robust Speech Systems , 2007, IEEE Trans. Speech Audio Process..
[118] Véronique Delvaux,et al. Discriminant analysis of nasal vs. oral vowels in French: comparison between different parametric representations , 2001, INTERSPEECH.
[119] M. Eskenazi,et al. The French language database: Defining, planning, and recording a large database , 1984, ICASSP.
[120] Edmund R. Malinowski,et al. Factor Analysis in Chemistry , 1980 .
[121] P. Woodland,et al. Discriminative linear transforms for speaker adaptation , 2001 .
[122] Lan Wang,et al. MPE-based discriminative linear transforms for speaker adaptation , 2008, Comput. Speech Lang..
[123] Andrew C. Morris,et al. Recent advances in the multi-stream HMM/ANN hybrid approach to noise robust ASR , 2005, Comput. Speech Lang..
[124] Daniel Jurafsky,et al. Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates , 2010, Speech Commun..
[125] Daniel Povey,et al. Large scale discriminative training of hidden Markov models for speech recognition , 2002, Comput. Speech Lang..
[126] Douglas D. O'Shaughnessy,et al. Speaker adaptation using evolutionary-based linear transform , 2006, INTERSPEECH.
[127] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[128] S.W. Shah,et al. Interactive Voice Response with Pattern Recognition Based on Artificial Neural Network Approach , 2007, 2007 International Conference on Emerging Technologies.
[129] Mark J. F. Gales,et al. MMI-MAP and MPE-MAP for acoustic model adaptation , 2003, INTERSPEECH.
[130] Yariv Ephraim,et al. A linear predictive front-end processor for speech recognition in noisy environments , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.
[131] Khaled Rasheed,et al. Guided crossover: a new operator for genetic algorithm based optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[132] Shaila D. Apte,et al. Speech and Audio Processing , 2012 .
[133] João Paulo da Silva Neto,et al. Context dependent modelling approaches for hybrid speech recognizers , 2010, INTERSPEECH.
[134] David B. Fogel,et al. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence) , 2006 .
[135] Hugo Van hamme,et al. A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition , 2007, EURASIP J. Adv. Signal Process..
[136] Roberto Pieraccini,et al. A stochastic model of human-machine interaction for learning dialog strategies , 2000, IEEE Trans. Speech Audio Process..
[137] Jont B. Allen,et al. How do humans process and recognize speech? , 1993, IEEE Trans. Speech Audio Process..
[138] Joelle Pineau,et al. Spoken Dialogue Management Using Probabilistic Reasoning , 2000, ACL.
[139] Franco Scarselli,et al. Are Multilayer Perceptrons Adequate for Pattern Recognition and Verification? , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[140] Yuji Kawaguchi,et al. The production of French nasal vowels by advanced Japanese and Spanish learners of French: a corpus-based evaluation study , 2010 .
[141] Manny Rayner,et al. Adding intelligent help to mixed-initiative spoken dialogue systems , 2002, INTERSPEECH.