Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification
暂无分享,去创建一个
[1] George K. Kokkinakis,et al. Algorithm for clustering continuous density HMM by recognition error , 1996, IEEE Trans. Speech Audio Process..
[2] L. Baum,et al. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .
[3] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[4] L. R. Rabiner,et al. A probabilistic distance measure for hidden Markov models , 1985, AT&T Technical Journal.
[5] Tetsuo Kosaka,et al. Speaker-independent phone modeling based on speaker-dependent HMMs' composition and clustering , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[6] Mari Ostendorf,et al. HMM topology design using maximum likelihood successive state splitting , 1997, Comput. Speech Lang..
[7] Peter C. Cheeseman,et al. Bayesian Classification (AutoClass): Theory and Results , 1996, Advances in Knowledge Discovery and Data Mining.
[8] Gautam Biswas,et al. Clustering sequence data using hidden Markov model representation , 1999, Defense, Security, and Sensing.
[9] Biing-Hwang Juang,et al. HMM clustering for connected word recognition , 1989, International Conference on Acoustics, Speech, and Signal Processing,.
[10] Padhraic Smyth,et al. Clustering Sequences with Hidden Markov Models , 1996, NIPS.
[11] G. Casella,et al. Explaining the Gibbs Sampler , 1992 .
[12] 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.
[13] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[14] Francisco Casacuberta,et al. Learning the structure of HMM's through grammatical inference techniques , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[15] David L. Dowe,et al. Intrinsic classification by MML - the Snob program , 1994 .
[16] Kai-Fu Lee,et al. Context-independent phonetic hidden Markov models for speaker-independent continuous speech recognition , 1990 .
[17] Shigeki Sagayama,et al. A successive state splitting algorithm for efficient allophone modeling , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[18] Kay-Fu Lee,et al. Context-dependent phonetic hidden Markov models for speaker-independent continuous speech recognition , 1990, IEEE Trans. Acoust. Speech Signal Process..
[19] S. Chib. Marginal Likelihood from the Gibbs Output , 1995 .
[20] L. Wasserman,et al. Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .
[21] Jerry B. Weinberg,et al. ITERATE: A Conceptual Clustering Method for Knowledge Discovery in Databases , 1994 .
[22] Paul R. Cohen,et al. Discovering Dynamics Using Bayesian Clustering , 1999, IDA.
[23] Andreas Stolcke,et al. Best-first Model Merging for Hidden Markov Model Induction , 1994, ArXiv.
[24] Stephen M. Omohundro,et al. Best-First Model Merging for Dynamic Learning and Recognition , 1991, NIPS.