Bayesian networks for discrete observation distributions in speech recognition

IEEE Transactions on Audio, Speech and Language Processing

Volumn 19, Issue 6, 2011, Pages 1476-1489

  Miguel, Antonio ^a   Ortega, Alfonso ^a   Buera, Luis ^b   Lleida, Eduardo ^a  

^a UNIVERSITY OF ZARAGOZA   (Spain)

^b Speech Technology Group   (Spain)

Author keywords

Bayesian networks; expectationmaximization; graphical models; maximum likelihood

Indexed keywords

BASELINE SYSTEMS; DISCRETE RANDOM VARIABLES; EXPECTATIONMAXIMIZATION; FEATURE VECTORS; GAUSSIAN MIXTURES; GAUSSIAN MODEL; GRAPHICAL MODEL; INTER-FEATURE DEPENDENCIES; JOINT STATISTICS; MIXTURES OF GAUSSIANS; MULTI-MODAL; ROBUSTNESS AGAINST NOISE; SPEECH FEATURES; UNIMODAL DISTRIBUTION;

BAYESIAN NETWORKS; DISTRIBUTED PARAMETER NETWORKS; GRAPHIC METHODS; HIDDEN MARKOV MODELS; INFERENCE ENGINES; INTELLIGENT NETWORKS; MAXIMUM LIKELIHOOD; RANDOM VARIABLES;

SPEECH RECOGNITION;

EID: 79957755364     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2010.2092764     Document Type: Article

References (57)

1. A. Ljolje, "The importance of cepstral parameter correlations in speech recognition," Comput. Speech Lang., vol. 8, no. 3, pp. 223-232, 1994.
2. R. Koshiba, M. Tachimori, and H. Kanazawa, "A flexible method of creating HMM using block-diagonalization of covariance matrices," in Proc. 5th Int. Conf. Spoken Lang. Process. ICSLP, Sydney, Australia, 1998, vol. 1.
3. M. Gales, "Semi-tied covariance matrices for hidden Markov models," IEEE Trans. Speech Audio Process., vol. 7, no. 3, pp. 272-281, May 1999.
4. E. Castillo, J. Gutirrez, and A. Hadi, Expert Systems and Probabilistic Network Models. New York: Springer-Verlag, 1997.
5. J. Pearl, Probabilistic Reasoning in Intelligent Systems. San Francisco, CA: Morgan Kaufmann, 1988.
6. S. Lauritzen, Graphical Models. New York: Oxford Univ. Press, 1996.
7. , M. I. Jordan, Ed., Learning in Graphical Models. Cambridge, MA: MIT Press, 1999.
8. A. Darwiche, Modeling and Reasoning With Bayesian Networks, 1st ed. New York: Cambridge Univ. Press, 2009.
9. J. Pearl, Causality: Models, Reasoning, and Inference. New York: Cambridge Univ. Press, 2000.
10. V. Digalakis, S. Tsakalidis, and L. Neumeyer, "Efficient speech recognition using subvector quantization and discrete-mixture HMMs," in Proc. ICASSP, 1999, pp. 569-572.
11. V. Digalakis, S. Tsakalidis, and L. Neumeyer, "Reviving discrete HMMs: The myth about the superiority of continuous HMMs," in EUROSPEECH'99, Budapest, Hungary, 1999, pp. 2463-2466.
12. J. Bilmes, "Natural statistical models for automatic speech recognition," Ph.D. dissertation, Dept. of Elect. Computer. Eng., Comput. Sci. Div., Univ. California Berkeley, , Berkeley, 1999.
13. J. Bilmes, "Factored sparse inverse covariance matrices," in Proc. ICASSP, 2000, pp. 1009-1012.
14. B. Jones and M. West, "Covariance decomposition in undirected Gaussian graphical models," Biometrika, vol. 92, no. 4, pp. 779-786, 2005. (Pubitemid 41681233)
15. K. M. Deviren and K. Daoudi, "Language modeling using dynamic Bayesian networks," in Proc. Lang. Resources Eval. Conf. (LREC), Lisbon, Portugal, May 2004.
16. G. Ji and J. Bilmes, "Dialog act tagging using graphical models," in Proc. ICASSP, Philadelphia, PA, Mar. 2005, pp. 33-36.
17. A. Miguel, A. Ortega, L. Buera, and E. Lleida, "Graphical models for discrete hidden Markov models in speech recognition," in Proc. Interspeech, 2009.
18. C. K. Chow and C. N. Liu, "Approximating discrete probability distributions with dependence trees," IEEE Trans. Inf. Theory, vol. IT-14, no. 3, pp. 462-467, May 1968.
19. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, no. 6, pp. 721-741, Nov. 1984. (Pubitemid 15453722)
20. X. Descombes, R. D. Morris, J. Zerubia, and M. Berthod, "Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood," IEEE Trans. Image Process., vol. 8, no. 7, pp. 954-963, Jul. 1999.
21. G. Gravier,M. Sigelle, and G. Chollet, "Markov random field modeling for speech recognition," Australian J. Intell. Inf. Process. Syst., vol. 5, no. 4, pp. 245-252, 1999.
22. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. 18th Int. Conf. Mach. Learn., 2001.
23. X. Zhu and Z. Ghahramani, "Towards semi-supervised classification with Markov random fields," Carnegie Mellon Univ., Pittsburgh, PA, 2002, Tech. Rep. CMU-CALD-02-106.
24. W. Buntine, "Theory refinement on Bayesian networks," in Proc. 7th Int. Conf. Uncertainty Artif. Intell., 1991, vol. 1, pp. 52-60.
25. W. Lam and F. Bacchus, "Learning Bayesian belief networks: An approach based on the MDL principle," Comput. Intell., vol. 10, pp. 269-293, 1994.
26. M. Meila and M. Jordan, "Learning with mixtures of trees," J. Mach. Learn. Res., vol. 1, pp. 1-48, 2000. (Pubitemid 33739061)
27. D. Chickering, "Learning Bayesian networks is np-complete," in Learning From Data: Artificial Intelligence and Statistics V. New York: Springer-Verlag, 1996, pp. 121-130.
28. J. Rissanen, "Modeling by shortest data description," Automatica, vol. 14, no. 5, pp. 465-471, 1978.
29. D. Heckerman, D. Geiger, and D. M. Chickering, "Learning Bayesian networks: The combination of knowledge and statistical data," Mach. Learn., vol. 20, pp. 197-243, 1995.
30. G. F. Cooper and E. Herskovits, "A Bayesian method for the induction of probabilistic networks from data," Mach. Learn., vol. 9, no. 4, pp. 309-347, 1992.
31. M. Sahami, "Learning limited dependence Bayesian classifiers," in Proc. 2nd Int. Conf. Knowl. Discov. Data Mining KDD-96, 1996, pp. 335-338.
32. D. M. Chickering, "Optimal structure identification with greedy search," J. Mach. Learn. Res., vol. 3, pp. 507-554, 2002.
33. J. Cheng, R. Greiner, J. Kelly, D. A. Bell, and W. Liu, "Learning Bayesian networks from data: An information-theory based approach," Artif. Intell., vol. 137, pp. 43-90.
34. K. Sood and M. Koivisto, "Exact Bayesian structure discovery in Bayesian networks," J. Mach. Learn. Res., vol. 5, pp. 549-573, 2004.
35. T. Silander and P. Myllymaki, "A simple approach for finding the globally optimal Bayesian network structure," in Proc. 22nd Conf. Uncertainty Artif. Intell. (UAI-2006), 2006, pp. 445-452.
36. N. Friedman, N. Friedman, D. Geiger, M. Goldszmidt, G. Provan, P. Langley, and P. Smyth, "Bayesian network classifiers," Mach. Learn., vol. 29, pp. 131-163, 1997. (Pubitemid 127510036)
37. M. G. Madden, "On the classification performance of TAN and general Bayesian networks," Knowl.-Based Syst., vol. 22, no. 7, pp. 489-495, 2009.
38. B. Thiesson, C. Meek, D. M. Chickering, and D. Heckerman, "Learning mixtures of DAG models," in Proc. 14th Conf. Uncertainty Artif. Intell. (UAI'98)., San Francisco, CA, 1998, pp. 504-513.
39. 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. 1-21, 1977.
40. P. Bell and S. King, "Sparse Gaussian graphical models for speech recognition," in Proc. Interspeech, 2007.
41. Distributed Speech Recognition; Advanced Front-End Feature Extraction Algorithm; Compression Algorithms, ETSI ES 202 050 v1.1.1, Jul. 2002.
42. P. Cheeseman and J. Stutz, Bayesian Classication (AutoClass): Theory and Results. Menlo Park, CA: AAAI Press, 1995, pp. 153-180.
43. A. Juan and E. Vidal, "Bernoulli mixture models for binary images," in Proc. ICPR, Cambridge, U.K., Aug. 2004, vol. 2.
44. A. Giménez-Pastor and A. Juan-Císcar, "Bernoulli HMMs for off-line handwriting recognition," in Proc. of the 8th Int. Workshop Pattern Recognition Inf. Syst. (PRIS 2008), Barcelona, Spain, Jun. 2008, pp. 86-91.
45. L. R. Rabiner, A Tutorial onHMM and selected Applications in Speech Recognition. Burlington, MA: Morgan Kaufmann, 1988, ch. 6.1, pp. 267-295.
46. X. Huang, A. Acero, and H.-W. Hon, Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Upper Saddle River, NJ: Prentice-Hall, 2001.
47. B. H. Juang and L. R. Rabiner, "The segmental k-means algorithm for estimating the parameters of hidden Markov models," IEEE Trans. Accoust., Speech, Signal Process., vol. 38, no. 9, pp. 1639-1641, Sep. 1990.
48. D. Macho, L. Mauuary, B. Noi, Y. M. Cheng, D. Ealey, D. Jouvet, H. Kelleher, D. Pearce, and F. Saadoun, "Evaluation of a noise-robust DSR front-end on aurora databases," in Proc. ICSLP, 2002.
49. H. G. Hirsch and D. Pearce, "The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proc. ISCA ITRW ASR2000 Automatic Speech Recognition: Challenges for the Next Millennium, Paris, France, Sep. 2000, pp. 18-20.
50. E. Parzen, "On the estimation of probability density functions and mode," Ann. Math. Statist., vol. 33, pp. 1065-1076, 1962.
51. D. Yu, L. Deng, Y. Gong, and A. Acero, "A novel framework and training algorithm for variable-parameter hidden Markov models," IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 7, pp. 1348-1360, Sep. 2009.
52. N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian network classifiers," Mach. Learn., vol. 29, pp. 131-163, 1997. (Pubitemid 127510036)
53. A. Torre, A. M. Peinado, J. C. Segura, J. L. Pérez, M. C. Benítez, and A. Rubio, "Histogram equalization of the speech representation for robust speech recognition," IEEE Trans. Speech Audio Process., vol. 13, no. 3, pp. 355-366, May 2005. (Pubitemid 40666170)
54. J. C. Segura, M. C. Benítez, A. de la Torre, and A. Rubio, "Feature extraction from time-frequency matrices for robust speech recognition," in Proc. Eurospeech, Aalborg, Denmark, Sep. 2001, pp. 1625-1628.
55. A. Miguel, A. Ortega, L. Buera, and E. Lleida, "Local projections and support vector based feature selection in speech recognition," in Proc. Interspeech, Brighton, U.K., 2009.
56. J. Frankel, M. Wester, and S. King, "Articulatory feature recognition using dynamic Bayesian networks," Comput. Speech Lang., vol. 21, no. 4, pp. 620-640, Oct. 2007. (Pubitemid 46880190)
57. K. Livescu, O. Çetin, M. Hasegawa-Johnson, S. King, C. Bartels, N. Borges, A. Kantor, P. Lal, L. Yung, S. Bezman, Dawson-Haggerty, B. Woods, J. Frankel, M. Magimai-Doss, and K. Saenko, "Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU summer workshop," in Proc. ICASSP, Honolulu, HI, Apr. 2007, pp. 621-624.