Optimizing the performance of spoken language recognition with discriminative training

IEEE Transactions on Audio, Speech and Language Processing

Volumn 16, Issue 8, 2008, Pages 1642-1653

  Zhu, Donglai ^a   Li, Haizhou ^a   Ma, Bin ^a   Lee, Chin Hui ^b  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Classifier optimization; Detection error tradeoff (DET); Discriminative training; Spoken language recognition (SLR)

Indexed keywords

DETECTION ERROR TRADEOFF (DET); DETECTION PERFORMANCE; DIFFERENTIABLE FUNCTIONS; DISCRIMINATIVE TRAINING; GAUSSIAN MIXTURE MODEL; GENERALIZED PROBABILISTIC DESCENTS; LANGUAGE RECOGNITION; MODEL PARAMETERS; OBJECTIVE FUNCTIONS; PERFORMANCE METRICS; SPOKEN LANGUAGE RECOGNITION (SLR); SPOKEN LANGUAGES; TRADE-OFF CURVES;

CLASSIFIERS; COMMUNICATION CHANNELS (INFORMATION THEORY); GRAPH THEORY; LEARNING SYSTEMS; LINGUISTICS; MAXIMUM LIKELIHOOD; OPTIMIZATION; PHOTOLITHOGRAPHY; SPEECH RECOGNITION;

ERROR DETECTION;

EID: 70350444555     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2008.2005319     Document Type: Article

References (30)

1. J. P. Egan, Signal Dec.ection Theory and ROC Analysis. New York: Academic, 1975.
2. J. A. Swets, R. M. Dawes, and J. Monahan, "Better Dec.isions through science, " Sci. Amer., vol. 283, pp. 82-87, 2000.
3. T. Fawcett, "An introduction to ROC analysis, " Pattern Recognition Lett., vol. 27, pp. 861-874, 2006.
4. A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, "The DET curve in assessment of detection task performance, " in Proc. Eurospeech, 1997, vol. 4, pp. 1895-1898.
5. "The 2005 NIST language recognition evaluation plan." [Online]. Available: http: //www.nist.gov/speech/tests/lang/2005.
6. N. Brummer and J. Preez, "Application-independent evaluation of speaker detection, " Comput. Speech Lang., vol. 20, no. 2, pp. 230-275, 2006.
7. M. A. Zissman, "Comparison of four approaches to automatic language identification of telephone speech, " IEEE Trans. Speech Audio Process., vol. 4, no. 1, pp. 31-44, Jan. 1996.
8. H. Li, B. Ma, and C.-H. Lee, "A vector space modeling approach to spoken language identification, " IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 1, pp. 271-284, Jan. 2007.
9. L. Burget, P. Matejka, and J. Cernocky, "Discriminative training techniques for acoustic language identification, " in Proc. ICASSP, 2006, pp. I-209-I-212.
10. F. Castaldo et al., "Acoustic language identification using fast discriminative training, " in Proc. Interspeech, 2007, pp. 346-349.
11. R. Auckenthaler, M. Carey, and H. Lloyd-Thomas, "Score normalization for text-independent speaker verification systems, " Digital Signal Process., vol. 10, pp. 42-54, 2000.
12. N. Brummer and D. Leeuwen, "On calibration of language recognition scores, " in Proc. IEEE Odyssey-Speaker Lang. Recognition Workshop, 2006, pp. 1-8.
13. B.-H. Juang, W. Chou, and C.-H. Lee, "Minimum classification error rate methods for speech recognition, " IEEE Trans. Speech Audio Process., vol. 5, no. 3, pp. 257-265, May 1997.
14. L. Yan, R. Dodier, M. C. Mozer, and R. Wolniewicz, "Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney statistic, " in Proc. ICML, 2003, pp. 848-855.
15. S. Gao, W. Wu, C.-H. Lee, and T.-S. Chua, "A maximal figure-ofmerit (MFoM)-learning approach to robust classifier design for text categorization, " ACMTrans. Inf. Syst., vol. 24, no. 2, pp. 190-218, Apr. 2006.
16. R. Baeza-Yates and B. Ribeiro, Modern Information Retrieval. New York: ACM, 1999.
17. H. Li, B. Ma, and R. Tong, "Vector-based spoken language recognition using output coding, " in Proc. Interspeech, 2006, pp. 429-432.
18. B. Ma, H. Li, and R. Tong, "Spoken language recognition using ensemble classifiers, " IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 7, pp. 2053-2062, Sep. 2007.
19. M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York: Courier Dover, 1965.
20. L. R. Bahl, P. F. Brown, P. V. Souza, and R. L. Mercer, "Maximum mutual information estimation of hidden Markov model parameters for speech recognition, " in Proc. ICASSP, 1986, pp. I-49-I-52.
21. D. Povey and P. C.Woodland, "Minimum phone error and I-smoothing for improved discriminative training, " in Proc. ICASSP, 2002, pp. 105-108.
22. E. McDermott, "Discriminative training for speech recognition, " Ph.D. dissertation, School Eng., Waseda Univ., Tokyo, Japan, 1997.
23. X. He and W. Chou, "Minimum classification error linear regression for acoustic model adaptation of CDHMMs, " in Proc. ICASSP, 2003, pp. 556-559.
24. X. Li, H. Jiang, and C. Liu, "Large margin HMMs for speech recognition, " in Proc. ICASSP, 2005, pp. 513-516.
25. F. Sha and L. K. Saul, "Large margin Gaussian mixture modeling for phonetic classification and recognition, " in Proc. ICASSP, 2006, pp. 265-268.
26. C. M. Bishop, Neural Networks for Pattern Recognition. Gloucestershire, U.K.: Clarendon, 1995.
27. B. Ma, R. Tong, and H. Li, "Discriminative vector for spoken language recognition, " in Proc. ICASSP, 2007, pp. 15-20.
28. "Callfriend Corpus, Telephone speech of 15 different languages or dialects." [Online]. Available: http: //www.ldc.upenn.edu/Catalog/by-Type. jsp\#speech.telephone.
29. "The NIST language recognition evaluation." [Online]. Available: http://www.nist.gov/speech/tests/lang.
30. J. A. Snyman, Practical Mathematical Optimization. New York: Springer, 2005.