Large-vocabulary continuous speech recognition systems: A look at some recent advances

IEEE Signal Processing Magazine

Volumn 29, Issue 6, 2012, Pages 18-33

  Saon, George ^a   Chien, Jen Tzung ^b  

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

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC TELEPHONE SYSTEMS; AUTOMATION; CONTINUOUS SPEECH RECOGNITION; DEEP NEURAL NETWORKS; SPEECH; SPEECH PROCESSING; SPEECH TRANSMISSION; TRANSCRIPTION; VOCABULARY CONTROL;

BROADCAST NEWS TRANSCRIPTIONS; CHANNEL DISTORTIONS; CONVERSATIONAL TELEPHONE SPEECH; LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION; LARGE VOCABULARY SPEECH RECOGNITION; RECOGNITION ERROR; SPEAKER DEPENDENTS; SPEAKER INDEPENDENTS;

SPEECH RECOGNITION;

EID: 85032751472     PISSN: 10535888     EISSN: None     Source Type: Journal    
DOI: 10.1109/MSP.2012.2197156     Document Type: Article

References (118)

1. S. Chen, B. Kingsbury, L. Mangu, D. Povey, G. Saon, H. Soltau, and G. Zweig, "Advances in speech transcription at IBM under the DARPA EARS program," IEEE Trans. Speech Audio Processing, vol. 14, no. 5, pp. 1596-1608, 2006.
2. G. Evermann, H. Y. Chan, M. J. F. Gales, T. Hain, X. Liu, D. Mrva, L. Wang, and P. C. Woodland, "Development of the 2003 CU-HTK conversational telephone speech transcription system," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2004, pp. 249-252.
3. S. Matsoukas, J.-L. Gauvain, G. Adda, T. Colthurst, C. Kao, O. Kimball, L. Lamel, F. Lefevre, J. Ma, J. Makhoul, L. Nguyen, R. Prasad, R. Schwartz, H. Schwenk, and B. Xiang, "Advances in transcription of broadcast news and conversational telephone speech within the combined EARS BBN/LIMSI system," IEEE Trans. Speech Audio Processing, vol. 14, no. 5, pp. 1541-1556, 2006.
4. A. Stolcke, B. Chen, H. Franco, V. Gadde, M. Graciarena, M. Hwang, A. Mandal, N. Morgan, X. Lei, T. Ng, M. Ostendorf, K. Sonmez, A. Venkataraman, D. Vergyri, W. Wang, J. Zheng, and Q. Zhu, "Recent innovations in speech-to-text transcription at SRI/ICSI/UW," IEEE Trans. Audio Speech Lang. Processing, vol. 14, no. 4, pp. 1729-1744, 2006.
5. M. J. F. Gales, D. Y. Kim, P. C. Woodland, H. Y. Chan, D. Mrva, R. Sinha, and S. E. Tranter, "Progress in the CU-HTK broadcast news transcription system," IEEE Trans. Speech Audio Processing, vol. 14, no. 5, pp. 1513-1525, 2006. (Pubitemid 46547578)
6. L. Lamel, A. Messaoudi, and J.-L. Gauvain, "Improved acoustic modeling for transcribing Arabic broadcast data," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2007, pp. 2077-2080.
7. T. Ng, K. Nguyen, R. Zbib, and L. Nguyen, "Improved morphological decomposition for Arabic broadcast news transcription," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2009, pp. 4309-4312.
8. M. Noamany, T. Schaaf, and T. Schultz, "Advances in the CMU/InterACT Arabic GALE transcription system," in Proc. North American Chapter of the Association for Computational Linguistics-Human Language Technologies (NAACL-HLT), 2007, pp. 129-132.
9. H. Soltau, G. Saon, B. Kingsbury, H.-K. Kuo, L. Mangu, D. Povey, and A. Emami, "Advances in arabic speech transcription at IBM under the DARPA GALE program," IEEE Trans. Audio Speech Lang. Processing, vol. 17, no. 5, pp. 884-894, 2009.
10. D. Vergyri, A. Mandal, W. Wang, A. Stolcke, J. Zheng, M. Graciarena, D. Rybach, C. Gollan, R. Schlueter, K. Kirchhoff, A. Faria, and N. Morgan, "Development of the SRI/Nightingale Arabic ASR system," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2008, pp. 1437-1440.
11. S. M. Chu, D. Povey, H.-K. Kuo, L. Mangu, S. Zhang, Q. Shi, and Y. Qin, "The 2009 IBM GALE Mandarin broadcast transcription system," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2010, pp. 4374-4377.
12. C. Plahl, B. Hoffmeister, G. Heigold, J. Loof, R. Schlueter, and H. Ney, "Development of the GALE 2008 Mandarin LVCSR system," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2009, pp. 2307-2311.
13. R. Sinha, M. J. F. Gales, D. Y. Kim, X. Liu, K. C. Sim, and P. C. Woodland, "The CU-HTK Mandarin broadcast news transcription system," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2006, pp. 14-19.
14. S. Young, "A review of large-vocabulary continuous-speech recognition," IEEE Signal Processing Mag., vol. 13, no. 5, pp. 45-57, 1996.
15. G. Zweig and M. Picheny, "Advances in large vocabulary continuous speech recognition," Adv. Comput., vol. 60, pp. 249-291, 2004.
16. J. M. Baker, L. Deng, J. Glass, S. Khudanpur, C.-H. Lee, and D. O'Shaughnessy, "Developments and directions in speech recognition and understanding-Part 1," IEEE Signal Processing Mag., vol. 26, no. 3, pp. 75-80, 2009.
17. J. M. Baker, L. Deng, S. Khudanpur, C.-H. Lee, J. R. Glass, N. Morgan, and D. O'Shaughnessy, "Updated minds report on speech recognition and understanding-Part 2," IEEE Signal Processing Mag., vol. 26, no. 4, pp. 78-85, 2009.
18. H. Hermansky, "Perceptual linear predictive (PLP) analysis of speech," J. Acoust. Soc. Am., vol. 87, no. 4, pp. 1738-1752, 1990. (Pubitemid 20256470)
19. S. Furui, "Speaker independent isolated word recognition using dynamic features of speech spectrum," IEEE Trans. Acoust., Speech, Signal Processing, vol. 34, no. 1, pp. 52-59, 1986. (Pubitemid 16575387)
20. G. Saon, M. Padmanabhan, R. Gopinath, and S. Chen, "Maximum likelihood discriminant feature spaces," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2000, pp. 1129-1132.
21. M. J. F. Gales, "Maximum likelihood multiple subspace projections for hidden Markov models," IEEE Trans. Speech Audio Processing, vol. 10, no. 2, pp. 37-47, 2002. (Pubitemid 34295263)
22. N. Kumar and A. G. Andreou, "Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition," Speech Commun., vol. 26, no. 4, pp. 283-297, 1998. (Pubitemid 128425471)
23. M. J. F. Gales, "Maximum likelihood linear transformations for HMM-based speech recognition," Comput. Speech Lang., vol. 12, no. 2, pp. 75-98, 1998. (Pubitemid 128383747)
24. S. Furui, "Recent advances in robust speech recognition," in Proc. Workshop Robust Speech Recognition for Unknown Communication Channels, 1997, pp. 11-20.
25. L. Deng, A. Acero, M. Plumpe, and X. Huang, "Large-vocabulary speech recognition under adverse acoustic environments," in Proc. Int. Conf. Spoken Language Processing (ICSLP), 2000, pp. 806-809.
26. H. Liao and M. J. F. Gales, "Issues with uncertainty decoding for noise robust automatic speech recognition," Speech Commun., vol. 50, no. 4, pp. 265-277, 2008.
27. F. Hilger and H. Ney, "Quantile based histogram equalization for noise robust large vocabulary speech recognition," IEEE Trans. Audio Speech Lang. Processing, vol. 14, no. 3, pp. 845-854, 2006. (Pubitemid 46547647)
28. L. Lee and R. Rose, "A frequency warping approach to speaker normalization," IEEE Trans. Speech Audio Processing, vol. 6, no. 1, pp. 49-60, 1998. (Pubitemid 128720631)
29. S. Wegmann, D. McAllaster, J. Orloff, and B. Peskin, "Speaker normalization on conversational telephone speech," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 1996, pp. 339-341.
30. G. Saon, S. Dharanipragada, and D. Povey, "Feature space Gaussianization," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2004, pp. 329-332.
31. L. Mangu, H.-K. Kuo, S. Chu, B. Kingsbury, G. Saon, H. Soltau, and F. Biadsy, "The IBM 2011 GALE Arabic speech transcription system," in Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2011, pp. 272-277.
32. D. Povey, B. Kingsbury, L. Mangu, G. Saon, H. Soltau, and G. Zweig, "fMPE: Discriminatively trained features for speech recognition," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2005, pp. 961-964.
33. D. Povey, D. Kanevsky, B. Kingsbury, B. Ramabhadran, G. Saon, and K. Visweswariah, "Boosted MMI for model and feature-space discriminative training," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2008, pp. 4057-4060.
34. H. Hermansky, D. P. W. Ellis, and S. Sharma, "Tandem connectionist feature extraction for conventional HMM systems," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2000, pp. 1635-1638.
35. F. Grezl, M. Karafiat, S. Kontar, and J. Cernocky, "Probabilistic and bottleneck features for LVCSR of meetings," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2007, pp. 757-760.
36. B. Kingsbury, H. Soltau, G. Saon, S. Chu, H. K. Kuo, L. Mangu, S. Ravuri, N. Morgan, and A. Janin, "The IBM 2009 GALE Arabic speech transcription system," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2011, pp. 4672-4675.
37. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, 1989.
38. S. Axelrod, R. Gopinath, and P. Olsen, "Modeling with a subspace constraint on inverse covariance matrices," in Proc. Int. Conf. Spoken Language Processing (ICSLP), 2002, pp. 2177-2180.
39. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc. B, vol. 39, no. 1, pp. 1-38, 1977.
40. B.-H. Juang, W. Chou, and C.-H. Lee, "Minimum classification error methods for speech recognition," IEEE Trans. Speech Audio Processing, vol. 5, no. 3, pp. 257-265, 1997. (Pubitemid 127745998)
41. L. R. Bahl, P. F. Brown, P. V. de Souza, and R. L. Mercer, "Maximum mutual information estimation of hidden Markov model parameters for speech recognition," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 1986, pp. 49-52.
42. L. Lamel, J.-L. Gauvain, and G. Adda, "Lightly supervised and unsupervised acoustic model training," Comput. Speech Lang., vol. 16, no. 1, pp. 115-129, 2002.
43. W. Reichl and G. Ruske, "Discriminative training for continuous speech recognition," in Proc. European Conf. Speech Communication and Technology (EUROSPEECH), 1995, pp. 537-540.
44. D. Povey and P. C. Woodland, "Minimum phone error and I-smoothing for improved discriminative training," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2002, pp. 105-108.
45. G. Saon, D. Povey, and G. Zweig, "Anatomy of an extremely fast LVCSR decoder," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2005, pp. 549-552. (Pubitemid 43908121)
46. J. Zheng and A. Stolcke, "Improved discriminative training using phone lattices," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2005, pp. 2125-2128. (Pubitemid 43908513)
47. B. Zhang and S. Matsoukas and R. Schwartz, "Discriminatively trained region dependent feature transforms for speech recognition," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2006, pp. 313-316.
48. G. Saon and D. Povey, "Penalty function maximization for large margin HMM training," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2008, pp. 920-923.
49. G. Saon, D. Povey, and H. Soltau, "Large margin semi-tied covariance transforms for discriminative training," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2009, pp. 3753-3756.
50. F. Sha and L. K. Saul, "Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 2007, pp. 313-316.
51. C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer-Verlag, 2006.
52. J. Li, M. Yuan, and C.-H. Lee, "Approximate test risk bound minimization through soft margin estimation," IEEE Trans. Audio Speech Lang. Processing, vol. 15, no. 8, pp. 2393-2404, 2007.
53. H. Jiang, X. Li, and C. Liu, "Large margin hidden Markov models for speech recognition," IEEE Trans. Audio Speech Lang. Processing, vol. 14, no. 5, pp. 1584-1595, 2006. (Pubitemid 46552926)
54. .J.-C. Chen and J.-T. Chien, "Bayesian large margin hidden Markov models for speech recognition," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2009, pp. 3765-3768.
55. X. He, L. Deng, and W. Chou, "Discriminative learning in sequential pattern recognition-A unifying review for optimization-oriented speech recognition," IEEE Signal Processing Mag., vol. 25, no. 5, pp. 14-36, 2008.
56. C. J. Leggetter and P. C. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models," Comput. Speech Lang., vol. 9, no. 2, pp. 171-185, 1995.
57. M. Padmanabhan, G. Saon, and G. Zweig, "Lattice-based unsupervised MLLR for speaker adaptation," in Proc. ITRI ASR2000: ASR Challenges for the New Millennium, 2000, pp. 128-132.
58. D. Povey and G. Saon, "Feature and model space speaker adaptation with full covariance Gaussians," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2006, pp. 1145-1148.
59. G. Saon, H. Soltau, U. Chaudhari, S. Chu, B. Kingsbury, H.-K. Kuo, L. Mangu, and D. Povey, "The IBM 2008 GALE Arabic speech transcription system," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2010, pp. 4378-4381.
60. H. Soltau, G. Saon, and B. Kingsbury, "The IBM Attila speech recognition toolkit," in Proc. IEEE Workshop Spoken Language Technology (SLT), 2010, pp. 97-102.
61. R. Kuhn, J.-C. Junqua, P. Nguyen, and N. Niedzielski, "Rapid speaker adaptation in eigenvoice space," IEEE Trans. Audio Speech Lang. Processing, vol. 8, no. 4, pp. 695-707, 2000. (Pubitemid 32025317)
62. K. Chen, W. Liau, H. Wang, and L.-S. Lee, "Fast speaker adaptation using eigenspace-based maximum likelihood linear regression," in Proc. Int. Conf. Spoken Language Processing (ICSLP), 2000, pp. 742-745.
63. J.-T. Chien and C.-H. Huang, "Aggregate a posteriori linear regression adaptation," IEEE Trans. Audio Speech Lang. Processing, vol. 14, no. 3, pp. 797-807, 2006. (Pubitemid 46547644)
64. A. Gunawardana and W. Byrne, "Discriminative speaker adaptation with conditional maximum likelihood linear regression," in Proc. European Conf. Speech Communication and Technology (EUROSPEECH), 2001, pp. 1203-1206.
65. L. Wang and P. C. Woodland, "MPE-based discriminative linear transforms for speaker adaptation," Comput. Speech Lang., vol. 22, no. 3, pp. 256-272, 2008. (Pubitemid 351329452)
66. J. Wu and Q. Huo, "A study of minimum classification error (MCE) linear regression for supervised adaptation of MCE-trained continuous-density hidden Markov models," IEEE Trans. Audio Speech Lang. Processing, vol. 15, no. 2, pp. 478-488, 2007.
67. M. J. F. Gales, "Robust continuous speech recognition using parallel model combination," IEEE Trans. Speech Audio Processing, vol. 4, no. 5, pp. 352-359, 1996. (Pubitemid 126753023)
68. A. Acero, L. Deng, T. Kristjansson, and J. Zhang, "HMM adaptation using vector Taylor series for noisy speech recognition," in Proc. Int. Conf. Spoken Language Processing (ICSLP), 2000, pp. 869-872.
69. G. E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition," IEEE Trans. Audio Speech Lang. Processing, vol. 20, no. 1, pp. 30-42, 2012.
70. F. Seide, G. Li, X. Chen, and D. Yu, "Conversational speech transcription using context-dependent deep neural networks," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2011, pp. 437-440.
71. G. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006. (Pubitemid 44024729)
72. A. Mohamed, G. E. Dahl, and G. Hinton, "Acoustic modeling using deep belief networks," IEEE Trans. Audio Speech Lang. Processing, vol. 20, no. 1, pp. 14-22, 2012.
73. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Processing Mag. vol. 29, no. 6, pp. 82-97, 2012.
74. S. F. Chen and J. Goodman, "An empirical study of smoothing techniques for language modeling," Comput. Speech Lang., vol. 13, no. 4, pp. 359-394, 1999. (Pubitemid 30518216)
75. R. Kneser and H. Ney, "Improved backing-off for m-gram language modeling," in Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), 1995, pp. 181-184.
76. Y. W. Teh, "A hierarchical Bayesian language model based on Pitman-Yor processes," in Proc. Annu. Meeting of the Association for Computational Linguistics, 2006, pp. 985-992.
77. S. Huang and S. Renals, "Hierarchical Bayesian language models for conversational speech recognition," IEEE Trans. Audio Speech Lang. Processing, vol. 18, no. 8, pp. 1941-1954, 2010.
78. J. Bellegarda, "Exploiting latent semantic information in statistical language modeling," Proc. IEEE, vol. 88, no. 8, pp. 1279-1296, 2000.
79. D. Gildea and T. Hofmann, "Topic-based language models using EM," in Proc. European Conf. Speech Communication and Technology (EUROSPEECH), 1999, pp. 2167-2170.
80. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet allocation," J. Mach. Learn. Res., vol. 3, no. 1, pp. 993-1022, 2003.
81. Y. C. Tam and T. Schultz, "Dynamic language model adaptation using variational Bayes inference," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2005, pp. 5-8. (Pubitemid 43907987)
82. J.-T. Chien and C.-H. Chueh, "Dirichlet class language models for speech recognition," IEEE Trans. Audio Speech Lang. Processing, vol. 19, no. 3, pp. 482-495, 2011.
83. P. Brown, V. Della Pietra, P. D. Souza, J. Lai, and R. Mercer, "Class-based n-gram models of natural language," Comput. Linguist., vol. 18, no. 4, pp. 467-179, 1992.
84. R. Rosenfeld, "A maximum entropy approach to adaptive statistical language modeling," Comput. Speech Lang., vol. 10, no. 3, pp. 187-228, 1996.
85. J.-T. Chien, "Association pattern language modeling," IEEE Trans. Audio Speech Lang. Processing, vol. 14, no. 5, pp. 1719-1728, 2006. (Pubitemid 46552927)
86. J.-T. Chien and C.-H. Chueh, "Joint acoustic and language modeling for speech recognition," Speech Commun., vol. 52, no. 3, pp. 223-235, 2010.
87. S. F. Chen, "Shrinking exponential language models," in Proc. North American Chapter of the Association for Computational Linguistics-Human Language Technologies (NAACL-HLT), 2009, pp. 468-476.
88. Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, "A neural probabilistic language model," J. Mach. Learn. Res., vol. 3, no. 2, pp. 1137-1155, 2003.
89. H. Schwenk, "Continuous space language models," Comput. Speech Lang., vol. 21, no. 3, pp. 492-518, 2007. (Pubitemid 46367510)
90. T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, and S. Khudanpur, "Recurrent neural network based language model," in Proc. Annu. Conf. Int. Speech Communication Association (INTERSPEECH), 2010, pp. 1045-1048.
91. C. Chelba and F. Jelinek, "Structured language modeling," Comput. Speech Lang., vol. 14, no. 4, pp. 283-332, 2000.
92. H.-K. Kuo, L. Mangu, A. Emami, I. Zitouni, and Y.-S. Lee, "Syntactic features for Arabic speech recognition," in Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2009, pp. 327-332.
93. G. Saon, G. Zweig, B. Kingsbury, L. Mangu, and U. Chaudhari, "An architecture for rapid decoding of large vocabulary conversational speech," in Proc. European Conf. Speech Communication and Technology (EUROSPEECH), 2003, pp. 1977-1980.
94. M. Mohri, F. Perreira, and M. Riley, "Weighted finite state transducers in speech recognition," Comput. Speech. Lang., vol. 16, no. 1, pp. 69-88, 2002.
95. S. Kanthak, H. Ney, M. Riley, and M. Mohri, "A comparison of two LVR search optimization techniques," in Proc. Int. Conf. Spoken Language Processing (ICSLP), 2002, pp. 1309-1312.
96. H. Soltau and G. Saon, "Dynamic network decoding revisited," in Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2009, pp. 276-281.
97. S. Chen, "Compiling large-context decision trees into finite-state transducers," in Proc. European Conf. Speech Communication and Technology (EUROSPEECH), 2003, pp. 1169-1172.
98. J. Fiscus, "A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER)," in Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 1997, pp. 347-354.
99. G. Evermann and P. Woodland, "Posterior probability decoding, confidence estimation and system combination," in Proc. Speech Transcription Workshop, 2000.
100. 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, no. 4, pp. 373-400, 2000.
101. A. Ljolje, D. Hindle, M. Riley, and R. Sproat, "The AT&T LVCSR 2000 system," in Proc. NIST LVCSR W orkshop, 2000.
102. G. Zweig and P. Nguyen, "A segmental CRF approach to large vocabulary continuous speech recognitio n," in Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2009, pp. 152-157.
103. O. Siohan, B. Ramabhadran, and B. Kingsbury, "Constructing ensembles of ASR systems using randomiz ed decision trees," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2005, pp. 197-200.
104. G. Saon and H. Soltau, "Boosting systems for large vocabulary continuous speech recognition," Spee ch Commun., vol. 54, no. 2, pp. 212-128, 2012.
105. D. Povey, L. Burget, M. Agarwal, P. Akyazi, F. Kai, A. Ghoshal, O. Glembek, N. Goel, M. Karafiat, A. Rastrow, R. C. Rose, P. Schwartz, and S. Thomas, "The subspace Gaussian mixture models-A structured model for speech recognition," Comput. Speech Lang., vol. 25, no. 2, pp. 404-439, 2011.
106. M. J. F. Gales and K. Yu, "Canonical state models for automatic speech recognition," in Proc. Annu . Conf. Int. Speech Communication Association (INTERSPEECH), 2010, pp. 58-61.
107. P. A. Olsen and R. A. Gopinath, "Modeling inverse covariance matrices by basis expansion," IEEE Tr ans. Speech Audio Processing, vol. 12, no. 1, pp. 37-46, 2004.
108. T. N. Sainath, B. Ramabhadran, M. Picheny, D. Nahamoo, and D. Kanevsky, "Exemplar-based sparse rep resentation features: from TIMIT to LVCSR," IEEE Trans. Audio Speech Lang. Processing, vol. 19, no. 8, pp. 2598-2613, 2011.
109. G. Saon and J.-T. Chien, "Bayesian sensing hidden Markov models for speech recognition," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2011, pp. 5056-5059.
110. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, no. 6, pp. 211-244, 2001. (Pubitemid 33687203)
111. G. Saon and J.-T. Chien, "Some properties of Bayesian sensing hidden Markov models," in Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2011, pp. 65-70.
112. G. Saon and J.-T. Chien, "Discriminative training for Bayesian sensing hidden Markov models," in Proc. Int. Conf. Acoutics, Speech, and Signal Processing (ICASSP), 2011, pp. 5316-5319.
113. G. Saon and J.-T. Chien, "Bayesian sensing hidden Markov models," IEEE Trans. Audio Speech Lang. Processing, vol. 20, no. 1 , pp. 43-54, 2012.
114. S. Watanabe, Y. Minami, A. Nakamura, and N. Ueda, "Variational Bayesian estimation and clustering for speech recognition," IEEE Trans. Speech Audio Processing, vol. 12, no. 4, pp. 365-381, 2004.
115. .Y. Zhang, P. Liu, J.-T. Chien, and F. Soong, "An evidence framework for Bayesian learning of conti nuous-density hidden Markov models," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2009, pp. 3857-3860.
116. J.-T. Chien and S. Furui, "Predictive hidden markov model selection for speech recognition," IEEE Trans. Speech Audio Processing, vol. 13, no. 3, pp. 377-387, 2005. (Pubitemid 40666172)
117. D. M. Blei, T. L. Griffiths, and M. I. Jordan, "The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies," J. ACM, vol. 57, no. 2, p. article 7, 2010.
118. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky, "Bayesian nonparametric methods for learning Ma rkov switching processes," IEEE Signal Processing Mag., vol. 27, no. 6, pp. 43-54, 2010.