Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains

IEEE Transactions on Speech and Audio Processing

Volumn 2, Issue 2, 1994, Pages 291-298

  Lee, Chin Hui ^b  

^a UFR 919 Laboratoire d'Informatique Pour la Mécanique et les Sciences de l'Ingénieur   (France)

^b LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; FUNCTION EVALUATION; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY; SPEECH INTELLIGIBILITY; SPEECH RECOGNITION; VECTORS;

BAYESIAN LEARNING; FORWARD BACKWARD ALGORITHMS; MAXIMUM A POSTERIORI ESTIMATION; MAXIMUM LIKELIHOOD; MODEL ADAPTATION; MULTIVARIATE GAUSSIAN MIXTURE OBSERVATIONS; PARAMETER SMOOTHING; PARAMETER SPECIFICATION; PRIOR DENSITY ESTIMATION;

PARAMETER ESTIMATION;

EID: 0028419019     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/89.279278     Document Type: Article

References (31)

1. L. E. Baum, T. Petrie, G. Soules, and N. Weiss, “A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains,” Ann. Math. Stat., vol. 41, pp. 164–171, 1970.
2. L. E. Baum, “An inequality and associated maximization technique in statistical estimation for probabilistics functions of Markov processes,” Inequalities, vol. 3, pp. 1–8, 1972.
3. P. Brown, C. -H. Lee, and J. Spohrer, “Bayesian adaptation in speech recognition,” in Proc. ICASSP-83, 1983, pp. 761–764.
4. G. Darmois, “Sur les lois de probabilite & estimation exhaustive,” C. R. Acad. Sci., vol. 260, pp. 1265–1266, 1935.
5. M. DeGroot, Optimal Statistical Decisions. New York: McGraw-Hill, 1970.
6. A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Roy. Statist. Soc. Ser. B, vol. 39, pp. 1–38, 1977
7. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
8. M. Ferretti and S. Scarci, “Large-vocabulary speech recognition with speaker-adapted codebook and HMM parameters,” in Proc. EuroSpeech-89, 1989, pp. 154–156.
9. G. D. Fomey, “The Viterbi algorithm,” Proc. IEEE, vol. 61, pp. 268–278, Mar. 1973.
10. J. -L. Gauvain and C. -H Lee, “Bayesian learning of gaussian mixture densities for hidden Markov models,” inProc. DARPA Speech Natural Language Workshop (Pacific Grove), Feb. 1991.
11. J. -L. Gauvain and C. -H Lee, “MAP estimation of continuous density HMM: Theory and applications,” Proc. DARPA Speech Natural language Workshop Feb. 1992.
12. J.-L. Gauvain and C.-H, Lee, “Bayesian Learning for Hidden Markov Model with Gaussian Mixture State Observation Densities,” Speech Communication, vol. 11, nos. 2–3, June 1992.
13. H. -W. Hon, “Vocabulary-independent speech recognition: The VOCIND system,” Ph.D. Thesis, School of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, Mar. 1992.
14. N. L. Johnson and S. Kotz, Distribution in Statistics. New York: Wiley, 1972.
15. B. -H. Juang, “Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains,” AT&T Techn. J., vol. 64, no. 6, 1985.
16. B. -H. Juang and L. R. Rabiner, “The segmental A'-means algorithm for estimating parameters of hidden markov models,” IEEE Trans. Acoust. Speech Signal Processing, vol. 38, no. 9, Sept. 1990.
17. B. O. Koopman, “On distributions admitting a sufficient statistic,” Trans. Amer. Math. Soc., vol. 39, pp. 399–409, 1936.
18. L. F. Lamel and J. -L. Gauvain, “Cross-lingual experiments with phone recognition,” in Proc. IEEE ICASSP-93 (Minneapolis, MN), Apr. 1993, pp. 507–510, vol. 2.
19. C. -H. Lee, L. R. Rabiner, R. Pieraccini, and J. G. Wilpon, “Acoustic modeling for large vocabulary speech recognition,” Comput. Speech Language vol. 4, pp. 127–165, 1990.
20. C. -H. Lee, C. -H. Lin, and B. -H,. Juang, “A study on speaker adaptation of the parameters of continuous density hidden Markov models,” IEEE Trans. Acoust. Speech Signal Processing, vol. 39, no. 4, pp. 806–814, Apr. 1991.
21. C. -H. Lee, E. Giachin, L. R. Rabiner, R. Pieraccini, and A. E. Rosenberg, “Improved acoustic modeling for large vocabulary continous speech recognition,” Comput. Speech Language, vol. 6, pp. 103–127, 1992.
22. C. -H. Lee and J. -L. Gauvain, “Speaker adaptation based on map estimation of hmm parameters,” in Proc. IEEE ICASSP-93 (Minneapolis, MN), Apr. 1993, pp. 558–561, vol. 2.
23. L. R. Liporace, “Maximum likelihood estimation for multivariate observations of markov sources,” IEEE Trans. Inform. Theory, vol. IT-28, no. 5, pp. 729–734, Sept. 1982.
24. P. J. Price, W. Fisher, J. Bernstein, and D. Pallett, “A database for continuous speech recognition in a 1000-word domain,” Proc. ICASSP-88 (New York), Apr. 1988, pp. 651–654.
25. Y. V. Prohorov and Y. A. Rozanov, Probability Theory. New York: Springer-Verlag, 1969.
26. L. R. Rabiner, J. G. Wilpon, and B. -H. Juang, “A segmental A'-means training procedure for connected word recognition,” AT&T Tech. J., vol. 64, no. 3, pp. 21–40, May 1986.
27. C. R. Rao, Linear Statistical Inference and Its Applications. New York: Wiley, 1973, 2nd ed.
28. R. A. Redner and H. F. Walker, “Mixture densities, maximum likelihood and em algorithm,” SIAM Rev., vol. 26, no. 2, 195–239, 1984.
29. H. Robbins, “The empirical bayes approach to statistical decision problems,” Ann. Math. Stat., vol. 35, pp. 1–20, 1964.
30. R. Stern and M. Lasry, “Dynameic speaker adaptation for feature-based isolated word recognition,” IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-35 no. 6, June 1987.
31. R. Zelinski and F. Class, “A learning procedure for speaker-dependent word recognition systems based on sequential processing of input tokens,” in Proc. ICASSP-83 (Boston), 1983, pp. 1053–1056.