Training hidden markov models with multiple observations - Combinatorial method

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 22, Issue 4, 2000, Pages 371-377

  Li, Xiaolin ^a,c   Parizeau, Marc ^a,b   Plamondon, Réjean ^a,b  

^a CADlink Technology Corporation   (Canada)

^b UNIVERSITÉ LAVAL   (Canada)

^c ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

Author keywords

Baum Welch algorithm; Forward backward procedure; Hidden Markov model; Multiple observation training

Indexed keywords

BAUM-WELCH ALGORITHM; HIDDEN MARKOV MODELS (HMM); LEVINSON'S TRAINING EQUATIONS;

ALGORITHMS; LAGRANGE MULTIPLIERS; LEARNING SYSTEMS; MATHEMATICAL MODELS; STATISTICAL METHODS;

MARKOV PROCESSES;

EID: 0033722098     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.845379     Document Type: Article

References (23)

1. L.E. Baum and T. Pétrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains," Annals of Math. Statistics, vol. 37, pp. 1,554-1,563, 1966.
2. L.E. Baum and J.A. Egon, An Inequality with Applications to Statistical Estimation for Probabilistic Functions of a Markov Process and to a Model for Ecology," Bull. Amer. Meteorology Soc., vol. 73, pp. 360-363, 1967.
3. L.E. Baum and G.R. Sell, Growth Functions for Transformations on Manifolds," Pacific J. Math., vol. 27, no. 2, pp. 211-227, 1968.
4. L.E. Baum, T. Pétrie, G. Soûles, and N. Weiss, A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains," Annals of Math. Statistics, vol. 41, no. 1, pp. 164-171, 1970.
5. L.E. Baum, An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Processes," Inequalities, vol. 3, pp. 1-8, 1970.
6. C.F.J. Wu, On the Convergence Properties of the EM Algorithm," Annals of Statistics, vol. 11, no. 1, pp. 95-103, 1983.
7. S.E. Levinson, L.R. Rabiner, and M.M. Sondhi, An Introduction to the Application of the Theory of Probabilistic Functions of Markov Process to Automatic Speech Recognition," Bell System Technical J., vol. 62, no. 4, pp. 1,035-1,074, 1983.
8. L.R. Bahl, F. Jelinek, and R.L. Mercer, A Maximum Likelihood Approach to Continuous Speech Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, pp. 179-190, 1983.
9. L.R. Rabiner and S.E. Levinson, A Speaker-Independent, SyntaxDirected, Connected Word Recognition System Based on Hidden Markov Models and Level Building," IEEE Trans. Acoustic, Speech, and Signal Processing, vol. 33, no. 3, pp. 561-573, 1985.
10. L.R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, 1989.
11. K.-F. Lee, Automatic Speech Recognition-The Development of SPHINX System. Kluwer Academic, 1989.
12. L. Rabiner and B.H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, N.J.: Prentice Hall, 1993
13. M. Iwayama, N. Indurkhya, and H. Motoda, A New Algorithm for Automatic Configuration of Hidden Markov Models," Proc. Fourth Int'l Workshop Algorithmic Learning Theory (ALT '93), pp. 237-250, 1993.
14. A. Kaltenmeier, T. Caesar, J.M. Gloger, and E. Mandler, Sophisticated Topology of Hidden Markov Models for Cursive Script Recognition," Proc. Second Int'l Conf. Document Analysis and Recognition, pp. 139-142, 1993.
15. P. Baldi and Y. Chauvim, Smooth On-Line Learning Algorithms for Hidden Markov Models," Neural Computation, vol. 6, pp. 307-318, 1994.
16. S.B. Cho and J.H. Kim, An HMM/MLP Architecture for Sequence Recognition," Neural Computation, vol. 7, pp. 358-369, 1995.
17. J. Dai, Robust Estimation of HMM Parameters Using Fuzzy Vector Quantization and Parzen's Window," Pattern Recognition, vol. 28, no. 1, pp. 53-57, 1995.
18. A. Kundu, Y. He, and P. Bahl, Recognition of Handwritten Word: First and Second Order Hidden Markov Model Based Approach," Pattern Recognition, vol. 22, no. 3, pp. 283-297, Mar. 1989.
19. S.R. Veltman and R. Prasad, Hidden Markov Models Applied to On-Line Handwritten Isolated Character Recognition," IEEE Trans. Image Processing, vol. 3, no. 3, pp. 314-318, 1994.
20. E.J. Bellegarda, J.R. Bellegarda, D. Nahamoo, and K.S. Nathan, A Fast Statistical Mixture Algorithm for On-Line Handwriting Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 12, pp. 1,227-1,233, Dec. 1994.
21. G. Rigoll, A. Kosmala, J. Rottland, and C.H. Neukirchen, A Comparison between Continuous and Discrete Density Hidden Markov Models for Cursive Handwriting Recognition," Proc. Int'l Conf. Pattern Recognition (ICPR '96), pp. 205-209, 1996.
22. J. Hu, M.K. Brown, and W. Turin, HMM Based On-Line Handwriting Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 1,039-1,045, Nov. 1996.
23. X. Li, M. Parizeau, and R. Plamondon, Hidden Markov Model Multiple Observation Training," Technical Report EPM/RT-99/16, Nov. 1999.