[1] Yu Lu, Wu Lenan, Xie Jun, et al. Optimal state and branch sequence based parameter estimationof continuous hidden Markov model [J]. Journal of Southeast University (English Edition), 2005, 21 (2): 136-140. [doi:10.3969/j.issn.1003-7985.2005.02.004]

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Optimal state and branch sequence based parameter estimationof continuous hidden Markov model()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

21

Issue:

2005 2

Page:

136-140

Research Field:

Computer Science and Engineering

Publishing date:

2005-06-30

Info

Title:

Optimal state and branch sequence based parameter estimationof continuous hidden Markov model

Author(s):

Yu Lu¹;  2;  Wu Lenan¹;  Xie Jun³

¹Department of Radio Engineering, Southeast University, Nanjing 210096, China
² Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China
³ Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China

Keywords:

continuous hidden Markov model;  optimal state and branch sequence;  maximum likelihood;  convergence;  Viterbi algorithm

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2005.02.004

Abstract:

A parameter estimation algorithm of the continuous hidden Markov model is introduced and the rigorous proof of its convergence is also included.The algorithm uses the Viterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determine optimal state and branch sequences.Based on the optimal sequence, parameters are estimated with maximum-likelihood as objective functions.Comparisons with the traditional Baum-Welch and segmental K-means algorithms on various aspects, such as optimal objectives and fundamentals, are made.All three algorithms are applied to face recognition.Results indicate that the proposed algorithm can reduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

References:

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[2] Juang B H, Rabiner L R.The segmental K-means algorithm for estimating parameters of hidden Markov models [J].IEEE Trans on Acoustics Speech and Signal Processing, 1990, 38(9):1639-1641.
[3] Andrieu C, Doucet A.Simulated annealing for maximum a posteriori parameter estimation of hidden Markov models [J].IEEE Trans on Information Theory, 2000, 46(5):994-1004.
[4] Yishai A B, Burshtein D.A discriminative training algorithm for hidden Markov models [J].IEEE Trans on Speech and Audio Processing, 2004, 12(5):204-217.
[5] Juang B H, Levinson S E, Sondhi M M.Maximum likelihood estimation for multivariate mixture observations of Markov chains [J].IEEE Trans on Information Theory, 1986, 32(3):307-309.
[6] Rabiner L R, Juang B H, Levinson S E.Some properties of continuous hidden Markov model representations [J].AT& T Technical Journal, 1985, 64(6):1251-1269.
[7] Wu C F Jeff.On the convergence properties of the EM algorithm [J].The Annals of Statistics, 1983, 11(1):95-103.
[8] Chen Baolin.Optimization theory and algorithm [M].Beijing:Tsinghua University Press, 1989.287-299.(in Chinese)
[9] Kohir V V, Desai U B.Face recognition [A].In:Proceedings of the IEEE Symposium on Circuits and Systems [C].Geneva, Switzerland:Presses Polytechniques et Universitaires Romandes, 2000, 5:305-308.
[10] Bicego M, Castellani U, Murino V.Using hidden Markov models and wavelets for face recognition [A].In:Wemer Bob, ed.Proceedings of the 12th International Conference on Image Analysis and Processing [C].Mantova, Italy:IEEE Computer Society, 2003.52-56.

Memo

Memo:

Biographies: Yu Lu(1973—), female, graduate;Wu Lenan(corresponding author), male, doctor, professor, wuln@seu.edu.cn.

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