Random forests of phonetic decision trees for acoustic modeling in conversational speech recognition

IEEE Transactions on Audio, Speech and Language Processing

Volumn 16, Issue 3, 2008, Pages 519-528

  Xue, Jian ^a,b   Zhao, Yunxin ^a  

^a University of Missouri   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Acoustic modeling; Acoustic score combination; Model clustering; Phonetic decision trees (PDTs); Random forests (RFs)

Indexed keywords

ACOUSTIC MODELING; ACOUSTIC MODELS; CLUSTERING METHODS; CONFIDENCE SCORES; CONVERSATIONAL SPEECH RECOGNITION; GAUSSIAN DENSITY FUNCTIONS; MAXIMUM-LIKELIHOOD ESTIMATIONS; MULTIPLE MODELS; NOVEL TECHNIQUES; ON-LINE DATUM; PDT TECHNIQUES; PHONETIC DECISION TREES (PDTS); RANDOM FORESTS (RFS); RELATIVE ENTROPIES; TRAINING DATUM; WORD RECOGNITION;

DECISION TREES; DECODING; FACADES; LINGUISTICS; MAXIMUM LIKELIHOOD ESTIMATION; SPEECH ANALYSIS; SPEECH RECOGNITION; TELEMEDICINE;

ACOUSTICS;

EID: 51549086717     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2007.913036     Document Type: Article

References (32)

1. S. J. Young, J. J. Odell, and P. C. Woodland, "Tree-based state tying for high accuracy modeling," in Proc. ARPA Human Lang. Tech. Workshop, 1994, pp. 307-312.
2. C. Chesta, P. Laface, and F. Ravera, "Bottom-up and top-down state clustering for robust acoustic modeling," in Proc. Eurospeech. 1997. pp. 11-14.
3. W. Reichl and W. Chou, "Robust decision tree state tying for continuous speech recognition," IEEE Trans. Speech Audio Process., vol. 8. no. 5, pp. 555-566, Sep. 2000.
4. S. S. Chen and P. S. Gopalakrishnan, "Clustering via the Bayesian information criterion with applications in speech recognition," in Proc. ICASSP, 1997, pp. 645-648.
5. S. Wang and Y. Zhao, "Online Bayesian tree-structured transformation of HMMs with optimal model selection for speaker adaptation," IEEE Trans. Speech Audio Process., vol. 9, no. 6, pp. 663-677, Sep. 2001.
6. J.-T. Chien and S. Furui, "Predictive hidden Markov model selection for speech recognition," IEEE Trans. Speech Audio Process., vol. 13. no. 3, pp. 377-387, May 2005.
7. R.-S. Hu and Y. Zhao, "Knowledge-based adaptive decision tree state tying for conversational speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 7, pp. 2160-2168, Sep. 2007.
8. J. Xue and Y. Zhao, "Novel lookahead decision tree state tying for acoustic modeling," in Proc. ICASSP, 2007, pp. TV-1133-IV-1136.
9. O. Siohan, B. Ramabhadran, and B. Kingsbury, "Constructing ensembles of ASR systems using randomized decision ttees," in Proc. ICASSP, 2005, pp. I-197-1-200.
10. T. G. Dietterich, "Ensemble methods in machine learning," in Proc. 1st Int. Workshop Multiple Classifier Syst., 2000, pp. 1-15.
11. D. Madigan, A. E. Raftery, C. Volinsky, and J. Hoeting, "Bayesian model averaging," in Proc. AAAI Workshop Integrating Multiple Learned Models, 1996, pp. 77-83.
12. T. G. Dietterich and G. Bakiri, "Solving multiclass learning problems via error-correcting output codes," J. Artif. Intel!. Res., vol. 2. pp. 263-286, 1995.
13. L. Breiman, "Bagging predictors," Mach. Learn., vol. 24. no. 2, pp. 123-140, 1996.
14. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Set. vol. 55, no. 1-3, pp. 119-139, 1997.
15. J. H. Friedman. "Stochastic gradient boosting," Comput. Statist. Data Anal., vol. 38, no. 4, pp. 367-378, 2002.
16. A. Demiriz, K. P. Bennett, and J. Shawe-Taylor, "Linear programming boosting via column generation," Mach. Learn., vol. 46, pp. 225-254.
17. L. Breiman. "Random forests," Mach. Learn., vol. 45. no. 1, pp. 5-32, 2001.
18. Random Forests Classifier Description, Website of Leo Breiman. [Online]. Available: http://www.stat.berkeley.edu/~breiman/
19. J. G. Fiscus, "A post-processing system to yield reduced word error rate: Recognizer output voting error reduction (ROVER)," in Proc. IEEE ASRU Workshop, 1997, pp. 347-352.
20. J. Xue and Y. Zhao, "Random forest-based confidence annotation using novel features from confusion network," in Proc. ICASSP, 2006, pp. I-1149-I-1152.
21. T. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization," Mach. Learn., vol. 40, no. 2, pp. 139-157. 200.
22. "The R project for statistical computing," [Online], Available: http://www.r-project.org
23. "HTK Toolkit," U.K. [Online]. Available: http://htk.eng.cam.ac.
24. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. B. vol. 39. no. 1, pp. 1-38, 1977.
25. Y. Zhao, X. Zhang, R.-S. Hu, J. Xue, X. Li, L. Che, R. Hu, and L. Schopp, "An automatic captioning system for telemedicine," in Proc. ICASSP, 2006, pp. 1-957-1-960.
26. E. Bocchieri and B. K.-W. Mak, "Subspace distribution clustering hidden Markov model," IEEE Trans. Speech Audio Process., vol. 9, pp. 264-275, Mar. 2001.
27. X-B. Li, F. K. Soong, T. A. Myrvoll, and R.-H. Wang, "Optimal clustering and non-uniform allocation of Gaussian kernels in scalar dimension for HMM compression," in Proc. ICASSP, 2005, pp. 1-669-1-772.
28. D. Povey and P. C. Woodland, "Frame discrimination training of HMMs for large vocabulary speech recognition," in Proc. ICASSP. 1999, pp. 333-336.
29. X. Zhang, Y. Zhao, and L. Schopp, "A novel method of language modeling for automatic captioning in telemedicine," IEEE Trans. Inf. Technol. Biomed., vol. 11, no. 3, pp. 332-337, May 2007.
30. X. Li and Y. Zhao, "A fast and memory-efficient N-gram language model lookup method for large vocabulary continuous speech recognition," Comput. Speech Lang., vol. 21, pp. 1-25, 2007.
31. J. Xue and Y. Zhao, "Improved confusion network algorithm and shortest path search from word lattice," in Proc. ICASSP, 2005. pp. 853-856.
32. C. Breslin and M. J. F. Gales, "Complementary system generation using directed decision trees," in Proc. ICASSP. 2007. pp. IV-337-1V-340.