Speech production knowledge in automatic speech recognition.
暂无分享,去创建一个
Simon King | Karen Livescu | Erik McDermott | Mirjam Wester | Korin Richmond | Joe Frankel | E. McDermott | Karen Livescu | Joe Frankel | M. Wester | Korin Richmond | Simon King
[1] David A. Nix,et al. Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs , 1998, NIPS.
[2] C. Browman,et al. Articulatory Phonology: An Overview , 1992, Phonetica.
[3] Steven Greenberg,et al. An elitist approach to automatic articulatory-acoustic feature classification for phonetic characterization of spoken language , 2005, Speech Commun..
[4] Takashi Fukuda,et al. Distinctive phonetic feature extraction for robust speech recognition , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[5] Victor Zue,et al. The MIT SUMMIT Speech Recognition System: A Progress Report , 1989, HLT.
[6] Victor Zue,et al. The Collection and Preliminary Analysis of a Spontaneous Speech Database , 1989, HLT.
[7] Steven Greenberg,et al. INSIGHTS INTO SPOKEN LANGUAGE GLEANED FROM PHONETIC TRANSCRIPTION OF THE SWITCHBOARD CORPUS , 1996 .
[8] Jeff A. Bilmes,et al. Hidden-articulator Markov models: performance improvements and robustness to noise , 2000, INTERSPEECH.
[9] Jeung-Yoon Choi,et al. Detection of consonant voicing: a module for a hierarchical speech recognition system , 1999 .
[10] Noam Chomsky,et al. The Sound Pattern of English , 1968 .
[11] Trevor Darrell,et al. Production domain modeling of pronunciation for visual speech recognition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[12] James R. Glass,et al. Segmentation and modeling in segment-based recognition , 1997, EUROSPEECH.
[13] Jeff A. Bilmes,et al. Hidden-articulator Markov models for speech recognition , 2003, Speech Commun..
[14] Harriet J. Nock,et al. Techniques for modelling Phonological Processes in Automatic Speech Recognition , 2001 .
[15] Li Deng,et al. Target-directed mixture dynamic models for spontaneous speech recognition , 2004, IEEE Transactions on Speech and Audio Processing.
[16] Pinquier,et al. An event-based acoustic-phonetic approach for speech segmentation and E-set recognition , 2002 .
[17] Li Deng,et al. Coarticulation modeling by embedding a target-directed hidden trajectory model into HMM - MAP decoding and evaluation , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[18] Simon King,et al. Detection of symbolic gestural events in articulatory data for use in structural representations of continuous speech , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[19] Sacha Krstulovic. LPC-based inversion of the DRM articulatory model , 1999, EUROSPEECH.
[20] Patti Price,et al. The DARPA 1000-word resource management database for continuous speech recognition , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.
[21] Carol Y. Espy-Wilson,et al. Speech recognition based on phonetic features and acoustic landmarks , 2004 .
[22] E. Vajda. Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet , 2000 .
[23] Masaaki Honda,et al. Estimation of articulatory movements from speech acoustics using an HMM-based speech production model , 2004, IEEE Transactions on Speech and Audio Processing.
[24] Li Deng,et al. Data-driven model construction for continuous speech recognition using overlapping articulatory features , 2000, INTERSPEECH.
[25] Don McAllaster,et al. Fabricating conversational speech data with acoustic models: a program to examine model-data mismatch , 1998, ICSLP.
[26] Kuldip K. Paliwal,et al. Automatic Speech and Speaker Recognition: Advanced Topics , 1999 .
[27] Korin Richmond,et al. Estimating articulatory parameters from the acoustic speech signal , 2002 .
[28] Li Deng,et al. Initial evaluation of hidden dynamic models on conversational speech , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[29] James R. Glass,et al. Heterogeneous measurements and multiple classifiers for speech recognition , 1998, ICSLP.
[30] Simon King,et al. SVitchboard 1: Small Vocabulary Tasks from Switchboard 1 , 2005 .
[31] Frederick Jelinek,et al. Nonreciprocal data sharing in estimating HMM parameters , 1998, ICSLP.
[32] James R. Glass. Finding acoustic regularities in speech: applications to phonetic recognition , 1988 .
[33] James R. Glass,et al. Feature-based pronunciation modeling with trainable asynchrony probabilities , 2004, INTERSPEECH.
[34] Keiichi Tokuda,et al. Acoustic-to-articulatory inversion mapping with Gaussian mixture model , 2004, INTERSPEECH.
[35] Carol Y. Espy-Wilson,et al. Knowledge-based parameters for HMM speech recognition , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.
[36] Mirjam Wester,et al. Pronunciation modeling for ASR - knowledge-based and data-derived methods , 2003, Comput. Speech Lang..
[37] Ellen Eide,et al. A linguistic feature representation of the speech waveform , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[38] Philip Hoole,et al. Beyond 2D in articulatory data acquisition and analysis , 2003 .
[39] Nelson Morgan,et al. Dynamic pronunciation models for automatic speech recognition , 1999 .
[40] Simon King,et al. Asynchronous Articulatory Feature Recognition Using Dynamic Bayesian Networks , 2004 .
[41] Simon King,et al. Speech recognition in the articulatory domain: investigating an alternative to acoustic HMMs , 2001 .
[42] Michael I. Jordan,et al. Factorial Hidden Markov Models , 1995, Machine Learning.
[43] Jianwu Dang,et al. A physiological model of speech production and the implication of tongue-larynx interaction , 1994, ICSLP.
[44] Carol Y. Espy-Wilson,et al. Speech parameterization based on phonetic features: application to speech recognition , 1995, EUROSPEECH.
[45] David J. Spiegelhalter,et al. Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.
[46] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[47] Jeff A. Bilmes,et al. What HMMs Can Do , 2006, IEICE Trans. Inf. Syst..
[48] L Saltzman Elliot,et al. A Dynamical Approach to Gestural Patterning in Speech Production , 1989 .
[49] R. Reddy,et al. Feature extraction segmentation and labeling in the Harpy and Hearsay‐II systems , 1976 .
[50] Jonathan G. Fiscus,et al. Darpa Timit Acoustic-Phonetic Continuous Speech Corpus CD-ROM {TIMIT} | NIST , 1993 .
[51] Erik Mcdermott. Production models for speech recognition , 2004 .
[52] Martin J. Russell,et al. A multiple-level linear/linear segmental HMM with a formant-based intermediate layer , 2005, Comput. Speech Lang..
[53] Kate Hunicke-Smith,et al. Effect of Speaking Style on LVCSR Performance , 1996 .
[54] Li Deng,et al. Phonetic classification and recognition using HMM representation of overlapping articulatory features for all classes of English sounds , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.
[55] Mirjam Wester,et al. An elitist approach to articulatory-acoustic feature classification , 2001, INTERSPEECH.
[56] Alan Wrench,et al. Continuous speech recognition using articulatory data , 2000, INTERSPEECH.
[57] H. Wakita. Estimation of vocal-tract shapes from acoustical analysis of the speech wave: The state of the art , 1979 .
[58] Joe Frankel,et al. Linear dynamic models for automatic speech recognition , 2004 .
[59] James R. Glass,et al. Hidden feature models for speech recognition using dynamic Bayesian networks , 2003, INTERSPEECH.
[60] Geoffrey Zweig,et al. Speech Recognition with Dynamic Bayesian Networks , 1998, AAAI/IAAI.
[61] Simon King,et al. ASR - articulatory speech recognition , 2001, INTERSPEECH.
[62] Timothy J. Hazen,et al. Pronunciation modeling using a finite-state transducer representation , 2005, Speech Commun..
[63] Han Shu,et al. EM training of finite-state transducers and its application to pronunciation modeling , 2002, INTERSPEECH.
[64] I. Zlokarnik. Adding articulatory features to acoustic features for automatic speech recognition , 1995 .
[65] Trevor Darrell,et al. Visual speech recognition with loosely synchronized feature streams , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[66] Xiuyang Yu,et al. What kind of pronunciation variation is hard for triphones to model? , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[67] J.A. Bilmes,et al. Graphical model architectures for speech recognition , 2005, IEEE Signal Processing Magazine.
[68] James R. Glass. A probabilistic framework for segment-based speech recognition , 2003, Comput. Speech Lang..
[69] Li Deng,et al. A mixed-level switching dynamic system for continuous speech recognition , 2004, Comput. Speech Lang..
[70] Partha Niyogi,et al. Distinctive feature detection using support vector machines , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[71] Bernd Lochschmidt,et al. Acoustic-Phonetic Analysis Based on an Articulatory Model , 1982 .
[72] Harriet J. Nock,et al. Pronunciation modeling by sharing gaussian densities across phonetic models , 1999, EUROSPEECH.
[73] Jordan Cohen,et al. Vocal tract normalization in speech recognition: Compensating for systematic speaker variability , 1995 .
[74] 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).
[75] L Deng,et al. Spontaneous speech recognition using a statistical coarticulatory model for the vocal-tract-resonance dynamics. , 2000, The Journal of the Acoustical Society of America.
[76] Katrin Kirchhoff. Syllable-level desynchronisation of phonetic features for speech recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.
[77] C S Blackburn,et al. A self-learning predictive model of articulator movements during speech production. , 2000, The Journal of the Acoustical Society of America.
[78] J C Junqua,et al. The Lombard reflex and its role on human listeners and automatic speech recognizers. , 1993, The Journal of the Acoustical Society of America.
[79] Takashi Fukuda,et al. Noise-robust ASR by using distinctive phonetic features approximated with logarithmic normal distribution of HMM , 2003, INTERSPEECH.
[80] C. C. Goodyear,et al. On the use of neural networks in articulatory speech synthesis , 1993 .
[81] A. Liberman,et al. The motor theory of speech perception revised , 1985, Cognition.
[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] Ronald A. Cole,et al. Performing fine phonetic distinctions: templates versus features , 1990 .
[84] Andreas Zierdt,et al. Beyond 2 D in articulatory data acquisition and analysis , 2003 .
[85] Andrej Ljolje,et al. Automatic Generation of Detailed Pronunciation Lexicons , 1996 .
[86] G Papcun,et al. Inferring articulation and recognizing gestures from acoustics with a neural network trained on x-ray microbeam data. , 1992, The Journal of the Acoustical Society of America.
[87] Karen Livescu,et al. Feature-based pronunciation modeling for automatic speech recognition , 2005 .
[88] Ronald A. Cole,et al. New telephone speech corpora at CSLU , 1995, EUROSPEECH.
[89] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[90] Hervé Bourlard,et al. Speech recognition with auxiliary information , 2004, IEEE Transactions on Speech and Audio Processing.
[91] Li Deng,et al. Speech recognition using the atomic speech units constructed from overlapping articulatory features , 1994, EUROSPEECH.
[92] A. Juneja,et al. Speech segmentation using probabilistic phonetic feature hierarchy and support vector machines , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[93] Simon King,et al. An Articulatory Feature-Based Tandem Approach and Factored Observation Modeling , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[94] Florian Metze,et al. A flexible stream architecture for ASR using articulatory features , 2002, INTERSPEECH.
[95] Sam T. Roweis,et al. Data-driven production models for speech processing , 1999 .
[96] Simon King,et al. A hybrid ANN/DBN approach to articulatory feature recognition , 2005, INTERSPEECH.
[97] Rebecca Bates,et al. Speaker dynamics as a source of pronunciation variability for continuous speech recognition models , 2004 .
[98] Jeff A. Bilmes,et al. WHAT HMMS CAN'T DO , 2004 .
[99] Tanja Schultz,et al. Multilingual articulatory features , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[100] Masaaki Honda,et al. A model of articulator trajectory formation based on the motor tasks of vocal‐tract shapes , 1996 .
[101] V.W. Zue,et al. The use of speech knowledge in automatic speech recognition , 1985, Proceedings of the IEEE.
[102] R. G. Leonard,et al. A database for speaker-independent digit recognition , 1984, ICASSP.
[103] Christian Wellekens,et al. Dynamic lexicon using phonetic features , 2001, INTERSPEECH.
[104] Simon King,et al. An automatic speech recognition system using neural networks and linear dynamic models to recover and model articulatory traces , 2000, INTERSPEECH.
[105] Samy Bengio,et al. Automatic speech recognition using dynamic bayesian networks with both acoustic and articulatory variables , 2000, INTERSPEECH.
[106] Gernot A. Fink,et al. Combining acoustic and articulatory feature information for robust speech recognition , 2002, Speech Commun..
[107] Ken-ichi Iso. Speech recognition using dynamical model of speech production , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[108] Vassilios Digalakis,et al. Segment-based stochastic models of spectral dynamics for continuous speech recognition , 1992 .
[109] Daniel P. W. Ellis,et al. Tandem acoustic modeling in large-vocabulary recognition , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[110] Pietro Laface,et al. Automatic detection and description of syllabic features in continuous speech , 1976 .
[111] John S. Bridle,et al. The HDM: a segmental hidden dynamic model of coarticulation , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[112] Simon King,et al. Modelling the uncertainty in recovering articulation from acoustics , 2003, Comput. Speech Lang..
[113] B. Atal,et al. Inversion of articulatory-to-acoustic transformation in the vocal tract by a computer-sorting technique. , 1978, The Journal of the Acoustical Society of America.
[114] Mark Hasegawa-Johnson,et al. Landmark-based speech recognition: report of the 2004 Johns Hopkins summer workshop , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[115] Shrikanth Narayanan,et al. An approach to real-time magnetic resonance imaging for speech production. , 2003, The Journal of the Acoustical Society of America.
[116] Antti-Veikko I. Rosti,et al. Linear Gaussian Models for Speech Recognition , 2004 .
[117] Martin J. Russell,et al. Data-driven, nonlinear, formant-to-acoustic mapping for ASR , 2002 .
[118] John Scott Bridle,et al. Towards better understanding of the model implied by the use of dynamic features in HMMs , 2004, INTERSPEECH.
[119] Raymond D. Kent,et al. X‐ray microbeam speech production database , 1990 .
[120] Kevin Murphy,et al. Bayes net toolbox for Matlab , 1999 .
[121] Manish D. Muzumdar. Automatic acoustic measurement optimization for segmental speech recognition , 1996 .
[122] Katsuhiko Shirai,et al. Estimating articulatory motion from speech wave , 1986, Speech Commun..
[123] Andrew Wilson Howitt,et al. Vowel landmark detection , 1999, EUROSPEECH.
[124] R. Fox. Modularity and the Motor Theory of Speech Perception , 1994 .
[125] Hervé Bourlard,et al. Connectionist Speech Recognition: A Hybrid Approach , 1993 .
[126] Takashi Fukuda,et al. Noise-robust automatic speech recognition using orthogonalized distinctive phonetic feature vectors , 2003, INTERSPEECH.
[127] Li Deng,et al. Coarticulation modeling by embedding a target-directed hidden trajectory model into HMM - model and training , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[128] Jianwu Dang,et al. Hybrid HMM/BN ASR system integrating spectrum and articulatory features , 2003, INTERSPEECH.
[129] Mark Hasegawa-Johnson,et al. Maximum mutual information based acoustic-features representation of phonological features for speech recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[130] Li Deng,et al. A generalized hidden Markov model with state-conditioned trend functions of time for the speech signal , 1992, Signal Process..
[131] V. Gracco,et al. Accurate recovery of articulator positions from acoustics: new conclusions based on human data. , 1996, The Journal of the Acoustical Society of America.
[132] Simon King,et al. Speech recognition via phonetically featured syllables , 1998, ICSLP.
[133] J.S. Suehle,et al. Impact of the trapping of anode hot holes on silicon dioxide breakdown , 2002, IEEE Electron Device Letters.
[134] Simon King,et al. Detection of phonological features in continuous speech using neural networks , 2000, Comput. Speech Lang..
[135] Simon King,et al. Articulatory feature recognition using dynamic Bayesian networks , 2007, Comput. Speech Lang..
[136] Paul Dalsgaard,et al. Multi-lingual label alignment using acoustic-phonetic features derived by neural-network technique , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[137] Katrin Kirchhoff,et al. Robust speech recognition using articulatory information , 1998 .
[138] James R. Glass,et al. Feature-based Pronunciation Modeling for Speech Recognition , 2004, HLT-NAACL.
[139] Mari Ostendorf,et al. Moving beyond the 'beads-on-a-string' model of speech , 1999 .
[140] Carol Y. Espy-Wilson,et al. An event-based acoustic-phonetic approach for speech segmentation and E-set recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[141] Martin J. Russell,et al. Probabilistic-trajectory segmental HMMs , 1999, Comput. Speech Lang..
[142] Heiga Zen,et al. Reformulating the HMM as a Trajectory Model , 2004 .
[143] M M Sondhi,et al. The potential role of speech production models in automatic speech recognition. , 1996, The Journal of the Acoustical Society of America.
[144] Eric Vatikiotis-Bateson,et al. Measuring and Modeling Speech Production , 1998 .
[145] George H. Freeman,et al. An HMM‐based speech recognizer using overlapping articulatory features , 1996 .
[146] Mark J. F. Gales,et al. Maximum margin training of generative kernels , 2004 .
[147] Ellen Eide. Distinctive features for use in an automatic speech recognition system , 2001, INTERSPEECH.