Tree-based covariance modeling of hidden markov models

IEEE Transactions on Audio, Speech and Language Processing

Volumn 14, Issue 6, 2006, Pages 2134-2146

  Tian, Ye ^a   Zhou, Jian Lai ^b   Lin, Hui ^c   Jiang, Hui ^d  

^a MICROSOFT   (United States)

^b MICROSOFT RESEARCH ASIA   (China)

^c TSINGHUA UNIVERSITY   (China)

^d YORK UNIVERSITY   (Canada)

Author keywords

Automatic speech recognition; Covariance modeling; Gaussian mixture models; Tree modeling

Indexed keywords

AUTOMATIC SPEECH RECOGNITION; COVARIANCE MATRICES; COVARIANCE MODELING; DATA SPARSENESS PROBLEMS; E-M ALGORITHMS; FULL COVARIANCE MODELING; GAUSSIAN; GAUSSIAN MIXTURE MODELS; HETEROSCEDASTIC; HIDDEN MARKOV MODELING; HIERARCHICAL STRUCTURES; INTERPOLATION COEFFICIENTS; INVERSE COVARIANCES; KULLBACK-LEIBLER DIVERGENCES; LINEAR DISCRIMINANT ANALYSIS; MATRIXES; MULTI LAYERS; MULTI-LAYERED; PARAMETRIC FORMS; RESOURCE MANAGEMENTS; ROOT NODES; TREE MODELING; TREE-BASED;

BLIND SOURCE SEPARATION; COMMUNICATION CHANNELS (INFORMATION THEORY); DISCRIMINANT ANALYSIS; HIDDEN MARKOV MODELS; INTERPOLATION; MAXIMUM LIKELIHOOD ESTIMATION; MIXTURES; OBJECT RECOGNITION; RESOURCE ALLOCATION; SPEECH ANALYSIS; SPEECH RECOGNITION; TREES (MATHEMATICS); TRELLIS CODES;

COVARIANCE MATRIX;

EID: 44449103265     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSA.2005.863210     Document Type: Article

References (31)

1. A. Ljolje, "The importance of cepstral parameter correlation in speech recognition," Comput. Speech Lang., vol. 8, pp. 223-232, 1994.
2. S. B. Davis and P. Mermelstein, "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-28, no. 4, p. 357, Aug. 1980.
3. K. Fukunaga, Introduction to Statistical Pattern Recognition. New York: Academic, 1972.
4. R. Haeb-Umbach and H. Ney, "Linear discriminant analysis for improved large vocabulary continuous speech recognition," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1992, vol. 1, pp. 13-16.
5. X. Aubert, R. Haeb-Umbach, and H. Ney, "Continuous mixture densities and linear discriminant analysis for improved context-dependent acoustic models," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1993, vol. 2, pp. 27-30.
6. G. Saon, M. Padmanabhan, R. Gopinath, and S. Chen, "Maximum likelihood discriminant feature spaces," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 2000, vol. 2, pp. 1129-1132.
7. R. A. Gopinath, "Maximum likelihood modeling with Gaussian distributions for classification," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1998, vol. 2, pp. 661-664.
8. N.Kumar, "Investigation of silicon-auditory models and generalization of linear discriminant analysis for improved speech recognition," Ph.D. dissertation, , Johns Hopkins Univ., Baltimore, MD, 1997.
9. X. Liu, M. F. J. Gales, and P. C. Woodland, "Automatic complexity control forHLDAsystems," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 2003, vol. 1, pp. 132-135.
10. B. Doherty, S.Vaseghi, and P. McCourt, "Full covariance modeling and adaptation in sub-bands," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 2000, vol. 2, pp. 969-972.
11. J. A. Bilmes, "Factored sparse inverse covariance matrices," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 2000, vol. 2, pp. 1009-1012.
12. M. J. F. Gales, "Semi-tied covariance matrices for hidden Markov models," IEEE Trans. Speech Audio Process., vol. 7, no. 3, pp. 272-281, May 1999.
13. -, "Maximum likelihood multiple subspace projections for hidden Markov models," IEEE Trans. Speech Audio Process., vol. 10, no. 2, pp. 37-47, Feb. 2002.
14. V. Vanhoucke and A. Sankar, "Mixtures of inverse covariance," IEEE Trans. Speech Audio Process., vol. 12, no. 3, pp. 250-264, May 2004.
15. P. A. Olsen and R. A. Gopinath, "Modeling inverse covariance matrices by basis expansion," IEEE Trans. Acoust., Speech, Signal Process., vol. 12, no. 1, pp. 37-46, Jan. 2004.
16. S. Axelrod, R. Gopinath, and P. Olsen, "Modeling with a subspace constraint on inverse covariance matrices," in Proc. Int. Conf. Spoken Language Processing, 2001, vol. 9, pp. 2177-2180.
17. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 7, no. 2, pp. 257-286, Feb. 1989.
18. K. Shinoda and C. H. Lee, "A structural Bayes approach to speaker adaptation," IEEE Trans. Speech Audio Process., vol. 9, no. 3, pp. 276-287, Mar. 2001.
19. M. Afify and O. Siohan, "Constrained maximum likelihood linear regression for speaker adaptation," in Proc. Int. Conf. Spoken Language Processing, 2000, pp. 861-864.
20. C. J. Leggetter and P. C. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models," Comput. Speech Lang., vol. 9, pp. 171-185, 1995.
21. T. Watanabe, K. Shinoda, K. Takagi, and E. Yamada, "Speech recognition using tree-structured probability density function," in Proc. Int. Conf. Spoken Language Processing, 1994, pp. 223-226.
22. G. Schwarz, "Estimating the dimension of a model," Ann. Statist., vol. 6, pp. 461-464, 1973.
23. W. H. Press, Numerical Recipes in C: The Art of Scientific Computing. Cambridge, U.K.: Cambridge University Press, 1986.
24. P. Price, W. Fisher, J. Bernstein, and D. Pallett, "A database for continuous speech recognition in a 1000-word domain," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1988, vol. 2, pp. 651-654.
25. S. Young, D. Kershaw, J. Odell, D. Ollason, V. Valtchev, and P.Woodland, The HTK Book (for HTK Version 3.0). : , 2000 [Online]. Available: http://htk.eng.cam.ac.uk/
26. E. Chang, Y. Shi, J. Zhou, and C. Huang, "Speech lab in a box: aMandarin speech toolbox to jump start speech related research toolbox," in Proc. Eur. Conf. Speech Communication and Technology, 2001, pp. 2782-2799.
27. E. Chang, J. Zhou, C. Huang, and K. F. Lee, "Large vocabulary Mandarin speech recognition with different approaches in modeling tones," in Proc. Int. Conf. Spoken Language Processing, 2000, pp. 983-986.
28. R. Morton, D. Whitehouse, and D. Ollason, The HAPI Book. Cambridge, U.K.: Entropic Cambridge Research Laboratory.
29. A.-V. I. Rosti and M. J. F. Gales, "Factor analyzed hidden Markov models for speech recognition," Comput. Speech Lang., vol. 18, no. 2, pp. 181-200, 2003.
30. H. Soltau, B. Kingsbury, L. Mangu, D. Povey, G. Saon, and G. Zweig, "The IBM 2004 conversational telephony system for rich transcription," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2005, vol. 1, pp. 205-208.
31. G. Saon, G. Zweig, B. Kingsbury, L. Mangu, and U. Chaudhari, "An architecture for rapid decoding of large vocabulary conversational speech," in Proc. Eur. Conf. Speech Communication and Technology, 2003, pp. 1977-1980.