Support vector machine training for improved hidden Markov modeling

IEEE Transactions on Signal Processing

Volumn 56, Issue 1, 2008, Pages 172-188

  Sloin, Alba ^a   Burshtein, David ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

Discriminative training; Hidden Markov model (HMM); Speech recognition; Support vector machine (SVM)

Indexed keywords

HIDDEN MARKOV MODELS; MAXIMUM LIKELIHOOD; SUPPORT VECTOR MACHINES; VITERBI ALGORITHM;

DISCRIMINATIVE TRAINING ALGORITHM;

SPEECH RECOGNITION;

EID: 37749027001     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2007.906741     Document Type: Article

References (31)

1. T. Jaakkola, M. Diekhans, and D. Haussler, "A discriminative framework for detecting remote protein homologies," J. Computat. Biol., vol. 7, pp. 95-114, 2000.
2. N. Smith and M. Gales, Speech Recognition Using SVMs, T. G. Dietterich, S. Becker, and Z. Ghahramani, Eds. Cambridge, MA: MIT Press, 2002, vol. 14, Adv. Neural Inf. Process. Syst..
3. V. Wan and S. Renals, "Speaker verification using sequence discriminant support vector machines," IEEE Trans. Speech Audio Process., vol. 13, no. 2, pp. 203-210, Mar. 2005.
4. C. Bahlmann, B. Haasdonk, and H. Burkhardt, "On-line handwriting recognition with support vector machines - A kernel approach," in Proc. 8th IWFHR, 2002, pp. 49-54.
5. J. Keshet, S. Shalev-Shwartz, Y. Singer, and D. Chazan, "Phoneme alignment based on discriminative learning," in 9th Europ. Conf. Speech Commun. Technol. (INTERSPEECH), 2005.
6. A. Ganapathiraju, J. E. Hamaker, and J. Picone, "Applications of support vector machines to speech recognition," IEEE Trans. Signal Process., vol. 52, pp. 2348-2355, Aug. 2004.
7. A. Sloin and D. Burshtein, "Support vector machine rescoring of hidden Markov models," presented at the 24th IEEE Conf. Elect. Electron. Eng., Eilat, Israel, Nov. 2006.
8. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
9. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowl. Discov., vol. 2, no. 2, pp. 121-167, 1998.
10. A. Ng, Cs229 Stanford Lecture Notes 2003 [Online]. Available: http:// www.stanford.edu/class/cs229/notes/cs229-notes3.pdf
11. J. C. Platt, Fast Training of Support Vector Machines Using Sequential Minimal Optimization, B. Scholkopf, C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT, 1999, vol. 12, Adv. Kernel Methods - Support Vector Learning, pp. 185-208.
12. C.-W. Hsu and C.-J. Lin, "A comparison of methods for multi-class support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 3, pp. 415-425, Mar. 2002.
13. V. Franc and V. Hlavác, "Multi-class support vector machine," in Proc. 16th Int. Conf. Pattern Recog. (ICPR'02), 2002, vol. 2, pp. 236-239.
14. O. L. Mangasarian and D. R. Musicant, "Successive overrelaxation for support vector machines," IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1032-1037, Sep. 1999.
15. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Statist. Soc., vol. 39, no. 1, pp. 1-38, 1977.
16. X. Huang, A. Acero, and H.-W. Hon, Spoken Language Processing. Englewood Cliffs, NJ: Prentice-Hall PTR, 2001.
17. L. R. Rabiner, J. G. Wilpon, and B.-H. Juang, "A segmental k-means training procedure for connected word recognition," AT&T Tech. J. pp. 21-40, May-Jun. 1986.
18. B.-H. Juang and L. R. Rabiner, "The segmental k-means algorithm for estimating parameters of hidden Markov models," IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no. 9, pp. 1639-1641, Sep. 1990.
19. L. R. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall PTR, 1993.
20. N. Merhav and Y. Ephraim, "Maximum likelihood hidden Markov modeling using a dominant sequence of states," IEEE Trans. Signal Process. vol. 39, no. 9, pp. 2111-2115, Sep. 1991.
21. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. 18th Int. Conf. Machine Learn. (ICML), MA, Jun. 2001.
22. B. Taskar, C. Guestrin, and D. Koller, "Max-margin Markov networks," in Adv. Neural Inf. Process. Syst. 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004.
23. Y. Altun, I. Tsochantaridis, and T. Hofmann, "Hidden Markov support vector machines," presented at the 20th Int. Conf. Mach. Learn. (ICML), Washington, DC, Aug. 2003.
24. L. Xu, D. Wilkinson, F. Southey, and D. Schuurmans, "Discriminative unsupervised learning of structured predictors," in Proc. 23rdInt. Conf. Mach. Learn., Pittsburgh, PA, Jun. 2006, pp. 1057-1064.
25. W. Xu, J. Wu, and Z. Huang, "A maximum margin discriminative learning algorithm for temporal signals," in Proc. 18th Int. Conf. Pattern Recogn. (ICPR'06), Hong Kong, Aug. 2006, vol. 2, pp. 460-463.
26. R. G. Leonard, "A database for speaker-independent digit recognition," in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), 1984, vol. 9, pp. 328-331.
27. HTK3 - Hidden Markov Model Toolkit Version 3.2.1,, 2002 [Online]. Available: http://htk.eng.cam.ac.uk
28. K. Murphy, Hidden Markov Toolbox for Matlab 1998 [Online]. Available: http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
29. Probabilistic Model Toolkit (PMT) HP Labs [Online]. Available: http:// www.hpl.hp.com/downloads/crl/Pmt/
30. J. Ma, Y. Zhao, S. Ahalt, and D. Eads, OSU SVM: A Support Vector Machine Toolbox for Matlab 2001 [Online]. Available: http://svm.sourceforge.net/ license.shtml
31. C.-C. Chang and C.-J. Lin, LIBSVM: A Library for Support Vector Machines 2001 [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm