A variable initialization approach to the em algorithm for better estimation of the parameters of Hidden Markov model based acoustic modeling of speech signals

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4065 LNAI, Issue , 2006, Pages 416-430

  Huda, Md Shamsul ^a   Ghosh, Ranadhir ^a   Yearwood, John ^a  

^a UNIVERSITY OF BALLARAT   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTICS; ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; SIGNAL PROCESSING; SPEECH PROCESSING;

ACOUSTIC MODELING; EXPECTATION-MAXIMIZATION (EM) ALGORITHMS; HIDDEN MARKOV MODELS (HMM); HMM INITIALIZATION PROCESSES;

PARAMETER ESTIMATION;

EID: 33746408179     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11790853_33     Document Type: Conference Paper

References (22)

1. Levinson S. E., Rabiner L.R., Sondhi M.M, An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition, The Bell System Technical Journal, 62, No 4 (1983).
2. Rabiner, L.R., A tutorial on Hidden Markov Models and selected applications in speech recognition, in Proc. of IEEE, 77 (1989) 257-286.
3. Dempster, A.P., Laird, N.M., and Rubin, D.B., Maximum likelihood from incomplete data via EM algorithm, Journal of royal statistical society. Series B. (Methodological), 39 (1977) 1-38.
4. Ghahramami, Z. and Jordan, M.I., Learning from incomplete data, Technical Report A1 Lab Memo No. 1509, CBCL Paper No. 108, MIT AI Lab,(1995).
5. Wu, C.F.J., On the convergence properties of the EM algorithm, The Annals of Statistics, 11 (1983) 95-103.
6. Xu, L. and Jordan, M.I., "On convergence properties of the EM algorithm for Gaussian mixtures". Neural Computation, 8 9 (1996) 129-151.
7. Chau C.W, Kwong S., Diu C.K., Fahrner W. R., Optimization of HMM by a Genetic Algorithm, In Proc. of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97).
8. Martinez, A.M. and Vitria, J., Learning mixture models using a genetic version of the EM algorithm". Pattern Recognition Letters, 21 (2000) 759-769.
9. Martinez, A. M. and Vitria, J., Clustering in image space for place recognition and visual annotations for human-robot interaction. IEEE Transactions on Systems, Man, and Cybernetics - Part B, 31(2001) 669-682.
10. Michalewicz, Z. and Schoenauer, M., Evolutionary Algorithms for Constrained Parameter Optimization Problems, Evolutionary Computation, Vol.4, No.1,(1996) 1-32.
11. Back T., Evolutionary Algorithm in Theory and Practice, Oxford University press (1996).
12. Back, T. and Schwefel, H., Evolutionary computation: An overview. In IEEE Conference on Evolutionary Computation,(1996) 20-29.
13. Pernkopf, F. and Bouchaffra, D., Genetic-Based EM Algorithm for Learning Gaussian Mixture Models, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol-27, No 28, (2005).
14. Kapadia, S., Valtchev, V. and Young, S.J., MMI training for continuous phoneme recognition on the TIMIT database, Proc. of the IEEE Conference on Acoustic Speech and Signal Processing , Vol. 2 (1993) 491-494.
15. Rabiner, L.R., Juang, B.H., Levisnon, S.E. and Sondhi, M.M., Some properties of continuous Hidden Markov Model representation, AT & T Tech. Journal, Vol. 64, 6 (1985) 1251-1270.
16. Soong F.K., Svendsen T., On the Automatic Segmentation of Speech, Proc. ICASSP, (1987).
17. Ghosh, R., Connection topologies for combining genetic and least square methods for neural learning, Journal of Intelligent System, Vol. 13, 3 (2004) 199-232.
18. Veterbi, A.J., Error bounds for convolutional codes and an asymptotically optimal decoding algorithm, IEEE transaction on Information Theory, vol. IT-13, (1967) 260-269.
19. Garofolo, S.J., Lamel, L., and Fisher, M.W., TIMIT Acoustic-Phonetic Continuous Speech Corpus, ISBN: 1-58563-019-5, Linguistic Data Consortium, University of Pennsylvania.
20. Yung, Y.S., and Oh, Y.H., A segmental-feature HMM for continuous speech recognition based on a parametric trajectory model, Journal of Speech Communication, Vol 38, 1(2002) 115-130.
21. Lamel, L. and Gauvain J. L., High performance speaker-independent phone recognition using CDHMM, Proc. EUROSPEECH, (1993)121-124.
22. Figueiredo, M.A.T. and Jain, A.K., Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (2002) 1-16.