Semi-continuous hidden Markov models for speech signals

Baum, 1979, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Annals of Mathematics and Statistics, 41, 164, 10.1214/aoms/1177697196

Brown, 1987, Acoustic-phonetic modelling problem in automatic speech recognition

Chow, 1987, BYBLOS: The BBN continuous speech recognition system, 89

Dempster, 1977, Maximum-likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B (methodological), 39, 1, 10.1111/j.2517-6161.1977.tb01600.x

Hasselblad, 1966, Estimation of parameters for a mixture of normal distributions, Technometrics, 8, 431, 10.2307/1266689

Huang, 1988, Performance comparison between semi-continuous and discrete hidden Markov models, IEE Electronics Letters, 24, 149, 10.1049/el:19880099

Huang, 1988, On several problems of hidden Markov models, 17

Huang, 1988, Maximum likelihood clustering applied to semi-continuous hidden Markov models for speech recognition, 71

Jelinek, 1976, Continuous speech recognition by statistical methods, 64, 532

Jelinek, 1985, The development of an experimental discrete dictation recognizer, 73, 1616

Jelinek, 1980, Interpolated estimation of Markov source parameters from sparse data

Juang, 1985, Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chain, AT & T Technical Journal, 64, 1235, 10.1002/j.1538-7305.1985.tb00273.x

Juang, 1985, Mixture autoregressive hidden Markov models for speech signals, IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-33, 1404, 10.1109/TASSP.1985.1164727

Lee, 1988, Large-vocabulary speaker-independent continuous speech recognition: The SPHINX system, 10.1016/0167-6393(88)90053-2

Levinson, 1986, Continuously variable duration hidden Markov models for automatic speech recognition, Computer Speech and Language, 1, 29, 10.1016/S0885-2308(86)80009-2

Levinson, 1983, An introduction to the application of theory of probabilistic functions of a Markov process to automatic speech recognition, Bell System Technical Journal, 62, 1035, 10.1002/j.1538-7305.1983.tb03114.x

Linde, 1980, An algorithm for vector quantizer design, IEEE Transactions and Communications, COM-28, 84, 10.1109/TCOM.1980.1094577

Makhoul, 1985, Vector quantisation in speech coding, 73, 1551

Nishimura, 1987, HMM-based speech recognition using multi-dimensional multilabeling, 1163

Parzen, 1962, On estimation of a probability density function and mode, Annals of Mathematics and Statistics, 33, 10.1214/aoms/1177704472

Poritz, 1986, On hidden Markov models in isolated word recognition, 705

Rabiner, 1986, An introduction to hidden Markov models, IEEE ASSP Magazine, 4, 10.1109/MASSP.1986.1165342

Rabiner, 1986, A segmental k-means training procedure for connected word recognition, AT & T Technical Journal, 65, 21, 10.1002/j.1538-7305.1986.tb00368.x

Rabiner, 1985, Recognition of isolated digits using hidden Markov models with continuous mixture densities, AT & T Technical Journal, 64, 1211, 10.1002/j.1538-7305.1985.tb00272.x

Redner, 1984, Mixture densities, maximum likelihood and the EM algorithm, SIAM review, 26, 195, 10.1137/1026034

Soudoplatoff, 1986, Markov modelling of continuous parameters in speech recognition, 45

Tseng, 1987, Fuzzy vector quantization applied to hidden Markov modelling, 641

Viterbi, 1967, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, IT-13, 260, 10.1109/TIT.1967.1054010