Discriminatively trained GMMs for language classification using boosting methods

IEEE Transactions on Audio, Speech and Language Processing

Volumn 17, Issue 1, 2009, Pages 187-197

  Siu, Man Hung ^a   Yang, Xi ^b   Gish, Herbert ^a  

^a BBN TECHNOLOGIES   (United States)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Boosting; Discriminative training; Language identification

Indexed keywords

BOOSTING; BOOSTING APPROACH; BOOSTING METHOD; CLASSIFICATION ,; CLASSIFICATION TREES; CONDITIONAL MODELS; DATA CLASSIFICATION; DISCRIMINATIVE MODELS; DISCRIMINATIVE TRAINING; GAUSSIAN MIXTURE MODELS; LANGUAGE IDENTIFICATION; MACHINE-LEARNING; MAXIMUM LIKELIHOOD CRITERION; NON-LINEAR ESTIMATION; PERFORMANCE IMPROVEMENTS; SPEECH APPLICATIONS; SPEECH MODELS; VERIFICATION TASK;

MAGNETOSTRICTIVE DEVICES; MAXIMUM LIKELIHOOD ESTIMATION; ROBOT LEARNING; SPEECH RECOGNITION;

LINGUISTICS;

EID: 70350437402     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2008.2006653     Document Type: Article

References (32)

1. R. Schapire, "The boosting approach to machine learning, an overview," in Nonlinear estimation and classification, D. D. Denison, M. H. Hansen, C. Holmes, B. Mallick, and B. Yu, Eds. New York: Springer, 2003.
2. L. Bahl, P. Brown, P. de Souza, and R. Mercer, "Maximum mutual information estimation of hidden Markov model parameters for speech recognition," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process.., 1986, vol.2, pp. 49-52.
3. V. Valtchev, J. Odell, P. Woodland, and S. Young, "MMIE training of large vocabulary recognition systems," Speech Commun., vol.22, pp. 303-314, 1997.
4. W. Chou, "Discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition," Proc. IEEE, vol.88, no.8, pp. 1201-1223, Aug. 2000.
5. D. Povey and P. Woodland, "Minimum phone error and I-smoothing for improved discriminative training," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2002, vol.1, pp. 105-108.
6. D. Povey, B. Kingsbury, L. Mangu, G. Saon, H. Soltau, and G. Zweig, "FMPE: Discriminatively trained features for speech recognition," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2005, vol.1, pp. 961-964.
7. V. Wan and W. Campbell, "Support vector machines for speaker verification and identification," in Proc. IEEE Signal Process. Soc. Workshop: Neural Netw. Signal Process., 2000, vol.2, pp. 775-784.
8. M. Siu and H. Gish, "Evaluation of word confidence for speech recognition system," Comput. Speech Lang., pp. 299-318, Oct. 1999.
9. J. Friedman, T. Hastie, and R. Tibshirani, Additive "Logistic Regression: A Statistical View of Boosting," 1998 [Online]. Available: http://www.citeseer.ist.psu.edu/friedman98additive.html.
10. J. Glass, "A probabilistic framework for segment-based speech recognition," Comput. Speech Lang., vol.17, pp. 137-152, 2003.
11. P. Matějka, P. Schwarz, L. Burget, and J. Černocký, "Use of anti-models to further improve state-of-art PRLM language recognition system," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2006, pp. IV-1005-IV-1008.
12. P. Torres-Carrasquillo, D. Reynolds, and J. D. , Jr., "Language identification using Gaussian mixture model tokenization," in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., 2002, pp. I-757-I-760.
13. W. Campbell, J. Campbell, D. Reynolds, E. Singer, and P. Torres-Carrasquillo, "Support vector machines for speaker and language recognition," Comput. Speech Lang., vol.20, pp. 210-229, 2006.
14. E. Noor and H. Aronowitz, "Efficient language identification using anchor models and support vector machines," in Proc. Odyssey: Speaker Lang. Recognition Workshop, 2006, pp. 1-6.
15. L. Burget, P. Matějka, and J. Černocký, "Discriminative training techniques for acoustic language identification," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2006, pp. I-209-I-212.
16. H. Li, B. Ma, and C.-H. Lee, "A vector space modeling approach to spoken language identification," IEEE Trans. Audio, Speech, Lang. Process., no.1, pp. 271-284, Jan. 2007.
17. H. Suo, M. Li, and Y. Yan, "Automatic language identification with discriminative language characterization based on SVM," Proc. IEICE Trans. Inf. Syst., pp. 567-575, 2008.
18. J. Fiscus, "A post-processing system to yield reduced word error rates: Recognizer output voting error reduction," in Proc. IEEE Workshop Autom. Speech Recognition and Understanding, 1997, pp. 347-354.
19. L. Mangu, E. Brill, and A. Stolcke, "Finding consensus in speech recognition: Word error minimization and other applications of confusion networks," Comput. Speech Lang., vol.14, pp. 373-400, 2000.
20. R. E. Schapire, "The strength of weak learnability," Mach. Learn., vol.5, no.2, pp. 197-227, 1990.
21. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol.55, no.1, pp. 119-139, August 1997.
22. J. Zhu, S. Rosset, H. Zou, and T. Hastie, Multi-Class Adaboost [Online]. Available: http://www-stat.stanford.edu/hastie/Papers/samme.pdf 2005
23. M. A. Zissman, "Automatic language identification using Gaussian mixture and hidden Markov models," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Apr. 1993, vol.2, pp. 399-402.
24. E. Singer, P. Torres-Carrasquillo, T. Gleason, W. Campbell, and D. Reynolds, "Acoustic, phonetic, and discriminative approaches to automatic language identification," in Proc. Eur. Conf. Speech Commun. Technol., Sep. 2003, pp. 1345-1348.
25. P. Torres-Carrasquillo, D. Reynolds, and J. D. , Jr., "Language identification using gassian mixture model tokenization," in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process., 2002, vol.1, pp. 757-761.
26. X. Yang, L. Zhai, M. Siu, and H. Gish, "Improved language identification using support vector machines for language modeling," in Proc. Int. Conf. Spoken Lang. Process., 2006, pp. 417-420.
27. P. Torres-Carrasquillo, M. K. E. Singer, R. Greene, D. Reynolds, and J. D. , Jr., "Approaches to language identification using Gaussian mixture models and shifted delta cepstral features," in Proc. Int. Conf. Spoken Lang. Process., 2002, pp. 89-92.
28. X. Yang, M. Siu, H. Gish, and B. Mak, "Boosting with anti-models for automatic language identification," in Proc. Eur. Conf. Speech Commun. Technol., 2007, pp. 342-348.
29. T. Joachims, "Making large-scale SVM learning practical," in Advances in Kernel Methods-Support Vector Learning. Cambridge, MA: MIT-Press, 1999.
30. W. Belfield and H. Gish, "A topic classification system based on parametric trajectory mixture models," in Proc. Eur. Conf. Speech Commun. Technol., 2003, pp. 1269-1272.
31. C. White, I. Shafran, and J.-L. Gauvain, "Discriminative classifiers for language recognition," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2006, pp. I-213-I-216.
32. F. Castaldo, D. Colibro, E. Dalmasso, P. Laface, and C. Vair, "Acoustic language identification using fast discriminative training," in Proc. Eur. Conf. Speech Commun. Technol., 2007, pp. 346-349.