Applications of broad class knowledge for noise robust speech recognition
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[1] David Pearce,et al. The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions , 2000, INTERSPEECH.
[2] John Dillenburg,et al. Techniques for improving the efficiency of heuristic search , 1993 .
[3] Andrew K. Halberstadt. Heterogeneous acoustic measurements and multiple classifiers for speech recognition , 1999 .
[4] Victor Zue,et al. Automatic language identification using a segment-based approach , 1993, EUROSPEECH.
[5] Mari Ostendorf,et al. From HMM's to segment models: a unified view of stochastic modeling for speech recognition , 1996, IEEE Trans. Speech Audio Process..
[6] Renato De Mori,et al. High performance connected digit recognition using maximum mutual information estimation , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[7] James R. Glass,et al. Recent progress in the MIT spoken lecture processing project , 2007, INTERSPEECH.
[8] Chin-Hui Lee,et al. Word recognition using whole word and subword models , 1989, International Conference on Acoustics, Speech, and Signal Processing,.
[9] Mark J. F. Gales,et al. The generation and use of regression class trees for MLLR adaptation , 1996 .
[10] Gunnar Fant,et al. Acoustic Theory Of Speech Production , 1960 .
[11] Tara N. Sainath,et al. Acoustic landmark detection and segmentation using the McAulay-Quatieri Sinusoidal Model , 2005 .
[12] Timothy J. Hazen. Visual model structures and synchrony constraints for audio-visual speech recognition , 2006, IEEE Transactions on Audio, Speech, and Language Processing.
[13] Tara N. Sainath,et al. Broad phonetic class recognition in a Hidden Markov model framework using extended Baum-Welch transformations , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).
[14] Xiaodong Cui,et al. MMSE-based stereo feature stochastic mapping for noise robust speech recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[15] Frank K. Soong,et al. A segment model based approach to speech recognition , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.
[16] Lawrence K. Saul,et al. Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[17] Hynek Hermansky,et al. RASTA processing of speech , 1994, IEEE Trans. Speech Audio Process..
[18] Giorgio Satta,et al. Computation of Probabilities for an Island-Driven Parser , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[19] Leonidas J. Guibas,et al. The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.
[20] Min Tang,et al. Modeling linguistic features in speech recognition , 2003, INTERSPEECH.
[21] Biing-Hwang Juang,et al. Speech recognition in adverse environments , 1991 .
[22] Benoît Maison,et al. Toward island-of-reliability-driven very-large-vocabulary on-line handwriting recognition using character confidence scoring , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[23] R Carlson,et al. Quarterly Progress and Status Report Phonetic and orthographic properties of the basic vocabulary of five European languages , 2007 .
[24] P. P. Chakrabarti,et al. Heuristic Search Through Islands , 1986, Artif. Intell..
[25] Stephen Cox,et al. Some statistical issues in the comparison of speech recognition algorithms , 1989, International Conference on Acoustics, Speech, and Signal Processing,.
[26] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[27] Dimitri Kanevsky. Extended Baum transformations for general functions , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[28] Julia Hirschberg,et al. V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.
[29] B. Atal. Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. , 1974, The Journal of the Acoustical Society of America.
[30] James R. Glass,et al. HETEROGENEOUS ACOUSTIC MEASUREMENTS FOR PHONETIC CLASSIFICATION , 1997 .
[31] Etienne Barnard,et al. Analysis of phoneme-based features for language identification , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.
[32] Chi-youn Park,et al. Consonant landmark detection for speech recognition , 2008 .
[33] Victor Zue,et al. A model of lexical access from partial phonetic information , 1984, ICASSP.
[34] D. Cox,et al. An Analysis of Transformations , 1964 .
[35] Sherif Abdou,et al. Beam search pruning in speech recognition using a posterior probability-based confidence measure , 2004, Speech Commun..
[36] A. House,et al. Toward automatic identification of the language of an utterance. I. Preliminary methodological con , 1977 .
[37] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[38] Tara N. Sainath,et al. Unsupervised Audio Segmentation using Extended Baum-Welch Transformations , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[39] Andrew J. Viterbi,et al. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.
[40] Stephanie Seneff,et al. Lexical stress modeling for improved speech recognition of spontaneous telephone speech in the jupiter domain , 2001, INTERSPEECH.
[41] Alex Acero,et al. Spoken Language Processing: A Guide to Theory, Algorithm and System Development , 2001 .
[42] 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.
[43] Günther Ruske,et al. A confidence-guided dynamic pruning approach - utilization of confidence measurement in speech recognition , 2005, INTERSPEECH.
[44] H. Pick,et al. Inhibiting the Lombard effect. , 1989, The Journal of the Acoustical Society of America.
[45] Gernot A. Fink,et al. Combining acoustic and articulatory feature information for robust speech recognition , 2002, Speech Commun..
[46] J. Wolf,et al. The HWIM speech understanding system , 1977 .
[47] Noam Chomsky,et al. The Sound Pattern of English , 1968 .
[48] William J. Byrne,et al. Noise robustness in the auditory representation of speech signals , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[49] H Hermansky,et al. Perceptual linear predictive (PLP) analysis of speech. , 1990, The Journal of the Acoustical Society of America.
[50] Victor Zue,et al. On organic interfaces , 2007, INTERSPEECH.
[51] Chin-Hui Lee,et al. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..
[52] Tara N. Sainath,et al. Audio classification using extended baum-welch transformations , 2007, INTERSPEECH.
[53] Michael Picheny,et al. Influence of background noise and microphone on the performance of the IBM Tangora speech recognition system , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[54] Abeer Alwan,et al. On the use of variable frame rate analysis in speech recognition , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[55] G. A. Miller,et al. An Analysis of Perceptual Confusions Among Some English Consonants , 1955 .
[56] Brian Kingsbury,et al. Evaluation of Proposed Modifications to MPE for Large Scale Discriminative Training , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[57] Tara N. Sainath,et al. Gradient steepness metrics using extended Baum-Welch transformations for universal pattern recognition tasks , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[58] VargaAndrew,et al. Assessment for automatic speech recognition II , 1993 .
[59] Yuqing Gao,et al. Noise reduction and speech recognition in noise conditions tested on LPNN-based continuous speech recognition system , 1993, EUROSPEECH.
[60] A. Nadas,et al. A generalization of the Baum algorithm to rational objective functions , 1989, International Conference on Acoustics, Speech, and Signal Processing,.
[61] Coarticulation • Suprasegmentals,et al. Acoustic Phonetics , 2019, The SAGE Encyclopedia of Human Communication Sciences and Disorders.
[62] Tara N. Sainath,et al. A Sinusoidal Model Approach to Acoustic Landmark Detection and Segmentation for Robust Segment-Based Speech Recognition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[63] V.W. Zue,et al. The use of speech knowledge in automatic speech recognition , 1985, Proceedings of the IEEE.
[64] Brian Kingsbury,et al. Boosted MMI for model and feature-space discriminative training , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[65] Daniel P. W. Ellis,et al. Using Broad Phonetic Group Experts for Improved Speech Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.
[66] W. A. Woods,et al. Language processing for speech understanding , 1986 .
[67] Timothy J. Hazen,et al. Pronunciation modeling using a finite-state transducer representation , 2005, Speech Commun..
[68] Jeff A. Bilmes,et al. Attention shift decoding for conversational speech recognition , 2007, INTERSPEECH.
[69] James R. Glass. A probabilistic framework for segment-based speech recognition , 2003, Comput. Speech Lang..
[70] L. Baum,et al. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .
[71] Philip C. Woodland,et al. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models , 1995, Comput. Speech Lang..
[72] Jordan Cohen,et al. The GALE project: A description and an update , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).
[73] Mark J. F. Gales,et al. Robust continuous speech recognition using parallel model combination , 1996, IEEE Trans. Speech Audio Process..
[74] Jonathan G. Fiscus,et al. Tools for the analysis of benchmark speech recognition tests , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[75] Yifan Gong,et al. Speech recognition in noisy environments: A survey , 1995, Speech Commun..
[76] Phil D. Green,et al. Robust automatic speech recognition with missing and unreliable acoustic data , 2001, Speech Commun..
[77] Victor Zue,et al. A* word network search for continuous speech recognition , 1993, EUROSPEECH.
[78] James R. Glass. Finding acoustic regularities in speech: applications to phonetic recognition , 1988 .
[79] Wayne A. Lea,et al. Trends in Speech Recognition , 1980 .
[80] Timothy J. Hazen,et al. Word and phone level acoustic confidence scoring , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[81] Stephanie Seneff,et al. Two-stage continuous speech recognition using feature-based models: a preliminary study , 2003, 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721).
[82] Kenneth N Stevens,et al. Toward a model for lexical access based on acoustic landmarks and distinctive features. , 2002, The Journal of the Acoustical Society of America.
[83] Tara N. Sainath,et al. A generalized family of parameter estimation techniques , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[84] Richard Lippmann,et al. Speech recognition by machines and humans , 1997, Speech Commun..
[85] Hakan Erdogan,et al. Incremental on-line feature space MLLR adaptation for telephony speech recognition , 2002, INTERSPEECH.
[86] Stan Davis,et al. Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .
[87] Mukund Padmanabhan,et al. Maximum-likelihood nonlinear transformation for acoustic adaptation , 2004, IEEE Transactions on Speech and Audio Processing.
[88] James R. Glass,et al. Real-time probabilistic segmentation for segment-based speech recognition , 1998, ICSLP.
[89] S. Boll,et al. Suppression of acoustic noise in speech using spectral subtraction , 1979 .
[90] James R. Glass,et al. Real-time telephone-based speech recognition in the Jupiter domain , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[91] James R. Glass,et al. Segmentation and modeling in segment-based recognition , 1997, EUROSPEECH.
[92] James R. Glass,et al. Heterogeneous measurements and multiple classifiers for speech recognition , 1998, ICSLP.
[93] S. J. Young,et al. Tree-based state tying for high accuracy acoustic modelling , 1994 .
[94] Jont B. Allen. How do humans process and recognize speech , 1993 .