Building acoustic model ensembles by data sampling with enhanced trainings and features

IEEE Transactions on Audio, Speech and Language Processing

Volumn 21, Issue 3, 2013, Pages 498-507

  Chen, Xin ^a   Zhao, Yunxin ^b  

^a PEARSON   (United States)

^b University of Missouri   (United States)

Author keywords

cross validation data sampling; discriminative training; Ensemble acoustic model; MLP feature; speaker clustering data sampling

Indexed keywords

ACOUSTIC MODEL; CROSS VALIDATION; DISCRIMINATIVE TRAINING; MLP FEATURE; SPEAKER CLUSTERING;

HIDDEN MARKOV MODELS; SPEECH RECOGNITION;

ARCHITECTURAL ACOUSTICS;

EID: 84872174281     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2012.2227729     Document Type: Article

References (32)

1. T. G. Dietterich, "Ensemble methods in machine learning," in Proc. MCS, 2000, pp. 1-15.
2. L. Breiman, "Random forests," Mach. Learn., vol. 45, pp. 5-32, 2001. (Pubitemid 32933532)
3. J. G. Fiscus, "A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER)," in Proc. IEEE ASRU Workshop, 1997, pp. 347-352.
4. T. Shinozaki and S. Furui, "Spontaneous speech recognition using a massively parallel decoder," in Proc. ICSLP, 2004, pp. 1705-1708.
5. O. Siohan, B. Ramabhadran, and B. Kingsbury, "Constructing ensembles of ASR systems using randomized decision trees," in Proc. ICASSP, 2005, pp. I-197-I-200.
6. R. Zhang et al., "Investigations on ensemble based semi-supervised acoustic model training," in Proc. EuroSpeech, 2005, pp. 1677-1680. (Pubitemid 43908402)
7. J. Billa, T. Colhurst, A. El-Jaroudi, R. Iyer, K. Ma, S. Matsoukas, C. Quillen, F. Richardson, M. Siu, G. Zavaliagkos, and H. Gish, "Recent experiments in large vocabulary conversational speech recognition," in Proc. ICASSP, 1999, vol. 1, pp. 41-44.
8. C. Bresline and M. J. F. Gales, "Building multiple complementary systems using directed decision tree," in Proc. Interspeech, 2007, pp. 1441-1444.
9. C. Meyer and H. Schramm, "Boosting HMM acoustic models in large vocabulary speech recognition," Speech Commun., vol. 48, pp. 532-548, 2006.
10. J. Xue and Y. Zhao, "Random forests of phonetic decision trees for acoustic modeling in conversational speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 3, pp. 519-528, Mar. 2008.
11. X. Cui, J. Xue, B. Xiang, and B. Zhou, "A study of bootstrapping with multiple acoustic features for improved automatic speech recognition," in Proc. Interspeech, 2009, pp. 240-243.
12. M. J. F. Gales, "Cluster adaptive training of hidden Markov models," IEEE Trans. Speech Audio Process., vol. 8, no. 4, pp. 417-428, Jul. 2000.
13. B.-H. Juang and S. Katagiri, "Discriminative learning for minimum error classification," IEEE Trans. Signal Process., vol. 40, no. 12, pp. 3043-3054, Dec. 1992. (Pubitemid 23603018)
14. L. R. Bahl, P. F. Brown, P.V.D. Souza, and R. L. Mercer, "Maximum mutual information estimation of hiddenMarkov model parameters for speech recognition," in Proc. ICASSP, 1986, pp. 49-52.
15. D. Povey and P. C.Woodland, "Minimumphone error and I-smoothing for improved discriminative training," in Proc. ICASSP, 2002, vol. 1, pp. 105-108.
16. H. Hermansky, D. P. W. Ellis, and S. Sharma, "Tandem connectionist feature stream extraction for conventional HMM systems," in Proc. ICASSP, 2000, vol. III, pp. 1635-1638.
17. Q. Zhu, A. Stolcke, B. Y. Chen, and N. Morgan, "Using MLP features in SRI's conversational speech recognition system," in Proc. ICSLP, 2005, vol. 2, pp. 921-924.
18. D. P. W. Ellis, "Improved recognition by combining different features and different systems," in Proc. AVIOS-2000, 2000.
19. T. Shinozaki and M. Ostendorf, "Cross-validation and aggregated EM training for robust parameter estimation," Comput. Speech Lang., vol. 22, no. 2, pp. 185-195, 2008. (Pubitemid 350016715)
20. S. J. Young, J. J. Odell, and P. C. Woodland, "Tree-based state tying for high accuracy modeling," in Proc. ARPA Human Lang. Technol. Workshop, 1994, pp. 307-312.
21. "HTK Toolkit," . Cambridge, U.K. [Online]. Available: http://htk. eng.cam.ac
22. L. Kaufman and P. J. Rousseeuw, Y. Dodge, Ed., "Clustering bymeans of medoids," in Statistical Data Analysis Based on the L1 Norm, 1987, pp. 405-416.
23. J. Hershey and P. Olsen, "Approximating the Kullback Leibler divergence between gaussian mixture models," in Proc. ICASSP, 2007, pp. 317-320.
24. A. Ko, R. Sabourin, and A. S. Britto, Jr, "A new HMMbased ensemble generation method for numeral recognition," in Proc. MCS Workshop, 2007, pp. 52-61. (Pubitemid 350270602)
25. X. Chen and Y. Zhao, "Data sampling ensemble acoustic modeling in speaker independent speech recognition," in Proc. ICASSP, 2010, pp. 5130-5133.
26. X. Chen and Y. Zhao, "Integrating MLP features and discriminative training in data sampling based ensemble acoustic modeling," in Proc. Interspeech, 2010, pp. 1349-1352.
27. K.-F. Lee and H.-W. Hon, "Speaker-independent phone recognition using hidden Markov models," IEEE Trans. Audio, Speech, Signal Process., vol. 37, no. 11, pp. 1641-1648, Nov. 1989.
28. 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. I-957-I-960.
29. N. Strom, "Phoneme probability estimationwith dynamic sparsely connected artificial networks," Free Speech J., no. 5, 1997.
30. X. Chen and Y. Zhao, "Data sampling based ensemble acoustic modeling," in Proc. ICASSP, 2009, pp. 3805-3808.
31. T. N. Sainath, B. Ramabhadran, and M. Picheny, "An exploration of large vocabulary tools for small vocabulary phonetic recognition," in Proc. IEEE ASRU Workshop, 2009.
32. A. R. Mohamed, G. Dahl, and G. E. Hinton, "Deep belief networks for phone recognition," in NIPS 22 Workshop Deep Learn. Speech Recognit., 2009.