A tutorial on hidden Markov models and selected applications in speech recognition

The fabric comprises a novel type of netting which will have particular utility in screening out mosquitoes and like insects and pests. The fabric is defined of voids having depth as well as width and length. The fabric is usable as a material from which to form clothing for wear, or bed coverings, or sleeping bags, etc., besides use simply as a netting.

[1]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[2]  L. Baum,et al.  An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .

[3]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[4]  L. Baum,et al.  Growth transformations for functions on manifolds. , 1968 .

[5]  F. Jelinek.  Fast sequential decoding algorithm using a stack , 1969 .

[6]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[7]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[8]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[9]  Lalit R. Bahl,et al.  Design of a linguistic statistical decoder for the recognition of continuous speech , 1975, IEEE Trans. Inf. Theory.

[10]  Lalit R. Bahl,et al.  Decoding for channels with insertions, deletions, and substitutions with applications to speech recognition , 1975, IEEE Trans. Inf. Theory.

[11]  J. Baker,et al.  The DRAGON system--An overview , 1975 .

[12]  F. Jelinek,et al.  Continuous speech recognition by statistical methods , 1976, Proceedings of the IEEE.

[13]  John E. Markel,et al.  Linear Prediction of Speech , 1976, Communication and Cybernetics.

[14]  R. Bakis.  Continuous speech recognition via centisecond acoustic states , 1976 .

[15]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[16]  Frederick Jelinek,et al.  Interpolated estimation of Markov source parameters from sparse data , 1980 .

[17]  Bruce Lowerre,et al.  The Harpy speech understanding system , 1990 .

[18]  A. B. Poritz,et al.  Linear predictive hidden Markov models and the speech signal , 1982, ICASSP.

[19]  Roberto Billi,et al.  Vector quantization and Markov source models applied to speech recognition , 1982, ICASSP.

[20]  Louis A. Liporace,et al.  Maximum likelihood estimation for multivariate observations of Markov sources , 1982, IEEE Trans. Inf. Theory.

[21]  Frederick Jelinek,et al.  25 Continuous speech recognition: Statistical methods , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[22]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[23]  Stephen E. Levinson,et al.  On the use of hidden Markov models for speaker‐independent recognition of isolated words from a medium size vocabulary , 1983 .

[24]  L. R. Rabiner,et al.  On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition , 1983, The Bell System Technical Journal.

[25]  Lalit R. Bahl,et al.  A Maximum Likelihood Approach to Continuous Speech Recognition , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  L. R. Rabiner,et al.  On the use of hidden Markov models for speaker-independent recognition of isolated words from a medium-size vocabulary , 1984, AT&T Bell Laboratories Technical Journal.

[27]  Rolf Carlson,et al.  ISOLATED WORD RECOGNITION , 1984 .

[28]  J. G. Wilpon,et al.  An improved word-detection algorithm for telephone-quality speech incorporating both syntactic and semantic constraints , 1984, AT&T Bell Laboratories Technical Journal.

[29]  B.-H. Juang,et al.  On the hidden Markov model and dynamic time warping for speech recognition — A unified view , 1984, AT&T Bell Laboratories Technical Journal.

[30]  L. R. Rabiner,et al.  Some properties of continuous hidden Markov model representations , 1985, AT&T Technical Journal.

[31]  B.-H. Juang,et al.  Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains , 1985, AT&T Technical Journal.

[32]  Biing-Hwang Juang,et al.  Mixture autoregressive hidden Markov models for speech signals , 1985, IEEE Trans. Acoust. Speech Signal Process..

[33]  Stephen E. Levinson,et al.  A speaker-independent, syntax-directed, connected word recognition system based on hidden Markov models and level building , 1985, IEEE Trans. Acoust. Speech Signal Process..

[34]  S.E. Levinson,et al.  Structural methods in automatic speech recognition , 1985, Proceedings of the IEEE.

[35]  R. Moore,et al.  Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[36]  L. R. Rabiner,et al.  A probabilistic distance measure for hidden Markov models , 1985, AT&T Technical Journal.

[37]  J. Makhoul,et al.  Vector quantization in speech coding , 1985, Proceedings of the IEEE.

[38]  John Makhoul,et al.  Context-dependent modeling for acoustic-phonetic recognition of continuous speech , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[39]  L. R. Rabiner,et al.  Recognition of isolated digits using hidden Markov models with continuous mixture densities , 1985, AT&T Technical Journal.

[40]  A. Poritz,et al.  On hidden Markov models in isolated word recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[41]  Biing-Hwang Juang,et al.  Maximum likelihood estimation for multivariate mixture observations of markov chains , 1986, IEEE Trans. Inf. Theory.

[42]  Stephen E. Levinson,et al.  Continuously variable duration hidden Markov models for automatic speech recognition , 1986 .

[43]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[44]  Biing-Hwang Juang,et al.  A model-based connected-digit recognition system using either hidden Markov models or templates , 1986 .

[45]  Lalit R. Bahl,et al.  Maximum mutual information estimation of hidden Markov model parameters for speech recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[46]  C. J. Wellekens,et al.  Global connected digit recognition using Baum-Welch algorithm , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[47]  S. Furui.  Speaker-Independent Isolated Word Recognition Based on Dynamics-Emphasized Cepstrum , 1986 .

[48]  Lawrence R. Rabiner,et al.  A segmental k-means training procedure for connected word recognition , 1986, AT&T Technical Journal.

[49]  Lalit R. Bahl,et al.  Experiments with the Tangora 20,000 word speech recognizer , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[50]  B. Merialdo.  Speech recognition with very large size dictionary , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[51]  Anne-Marie Derouault,et al.  Context-dependent phonetic Markov models for large vocabulary speech recognition , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[52]  Stephen E. Levinson,et al.  Continuous speech recognition by means of acoustic/ Phonetic classification obtained from a hidden Markov model , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[53]  Biing-Hwang Juang,et al.  On the use of bandpass liftering in speech recognition , 1987, IEEE Trans. Acoust. Speech Signal Process..

[54]  John Makhoul,et al.  BYBLOS: The BBN continuous speech recognition system , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[55]  D. B. Paul.  A speaker-stress resistant HMM isolated word recognizer , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[56]  E. A. Martin,et al.  Multi-style training for robust isolated-word speech recognition , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[57]  Jay G. Wilpon,et al.  Application of hidden Markov models to automatic speech endpoint detection , 1987 .

[58]  Yoh'ichi Tohkura,et al.  A weighted cepstral distance measure for speech recognition , 1987, IEEE Trans. Acoust. Speech Signal Process..

[59]  Vishwa Gupta,et al.  Integration of acoustic information in a large vocabulary word recognizer , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[60]  Kai-Fu Lee,et al.  On large-vocabulary speaker-independent continuous speech recognition , 1988, Speech Commun..

[61]  Frank K. Soong,et al.  On the use of instantaneous and transitional spectral information in speaker recognition , 1988, IEEE Trans. Acoust. Speech Signal Process..

[62]  Raj Reddy,et al.  Large-vocabulary speaker-independent continuous speech recognition: the sphinx system , 1988 .

[63]  Lawrence R. Rabiner,et al.  A minimum discrimination information approach for hidden Markov modeling , 1989, IEEE Trans. Inf. Theory.