Multi-level adaptive networks in tandem and hybrid ASR systems

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 6975-6979

  Bell, Peter ^a   Swietojanski, Pawel ^a   Renals, Steve ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

BBC; deep neural networks; hybrid; MLAN; tandem; TED

Indexed keywords

BBC; DEEP NEURAL NETWORKS; HYBRID; MLAN; TANDEM; TED;

SIGNAL PROCESSING; SPEECH RECOGNITION;

EXPERIMENTS;

EID: 84890537527     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6639014     Document Type: Conference Paper

References (34)

1. H. Bourlard and N. Morgan, Connectionist Speech Recognition: A Hybrid Approach, Kluwer Academic Publishers, 1994
2. S. Renals, N. Morgan, H. Bourlard, M. Cohen, and H. Franco, "Connectionist probability estimators in HMM speech recognition," IEEE Transactions on Speech and Audio Processing, vol. 2, no. 1, pp. 161-174, 1994
3. H. Hermanksy, D.P.W. Ellis, and S. Sharma, "Tandem connectionist feature extraction for conventional HMM systems," in Proc. ICASSP, 2000, pp. 1635-1630
4. Q. Zhu, A. Stolcke, B.Y. Chen, and N. Morgan, "Using MLP features in SRIs conversational speech recognition system," in Proc. Interspeech, 2005
5. F. Grezl, M Karafiat, S. Kontar, and J. Cernokcy, "Probabilistic and bottleneck features for LVCSR of meetings," in Proc. ICASSP, 2007
6. M. M. Hochberg, S. J. Renals, A. J. Robinson, and G. D. Cook, "Recent improvements to the ABBOT large vocabulary CSR system," in Proc. IEEE ICASSP, 1995, pp. 69-72
7. A. J. Robinson, G. D. Cook, D. P. W. Ellis, E. Fosler-Lussier, S. J. Renals, and D. A. G. Williams, "Connectionist speech recognition of broadcast news," Speech Communication, vol. 37, no. 1-2, pp. 27-45, 2002
8. S. J. Young and P. C. Woodland, "State clustering in hidden Markov model-based continuous speech recognition," Computer Speech &Language, vol. 8, no. 4, pp. 369-383, 1994
9. "Maximum likelihood linear transforms for HMM-based speech recognition," Computer Speech and Language, vol. 12, no. 75-98, 1998
10. D. Povey and P.C. Woodland, "Minimum phone error and I-smoothing for improved discriminative training," in Proc. ICASSP. IEEE, 2002, vol. I, pp. 105-108
11. D. Povey, B. Kingsbury, L. Mangu, G. Saon, H. Soltau, and G. Zweig, "FMPE: Discriminatively trained features for speech recognition," in Proc ICASSP, 2005
12. T. Hain, L. Burget, J. Dines, P.N. Garner, F. Grezl, A.E. Hannani, M. Huijbregts, M. Karafiat, M. Lincoln, and V. Wan, "Transcribing meetings with the AMIDA systems," IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 2, pp. 486-498, 2012
13. G.E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition," IEEE Transactions on Audio, Speech and Language Processing, vol. 20, no. 1, pp. 30-42, 2012
14. F. Seide, G. Li, X. Chen, and D. Yu, "Feature engineering in context-dependent deep neural networks for conversational speech transcription," in Proc. ASRU, 2011
15. A. Mohammed, G.E. Dahl, and Hinton G., "Acoustic modeling using deep belief networks," IEEE Transactions on Audio, Speech and Language Processing, vol. 20, no. 1, pp. 14-22, 2012
16. H. Bourlard, N. Morgan, C.Wooters, and S. Renals, "CDNN: A context dependent neural network for continuous speech recognition," in Proc. ICASSP, 1992, vol. 2, pp. 349-352
17. D. Kershaw, T. Robinson, and S. Renals, "The 1995 ABBOT LVCSR system for multiple unknown microphones," in Proc. ICSLP, 1996, pp. 1325-1328
18. G. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, pp. 1527-1554, 2006
19. F. Seide, G. Li, and D. Yu, "Conversational speech transcription using context-dependent deep neural networks," in Proc. Interspeech, 2011
20. T. Sainath, B. Kingsbury, B. Ramabhadran, P. Fousek, P. Novak, and A. Mohammed, "Making deep belief networks effective for large vocabulary continuous speech recognition," in Proc. ASRU, 2011
21. T. Sainath, B. Kingsbury, and B. Ramabhadran, "Auto-encoder bottleneck features using deep belief networks," in Proc ICASSP, 2012
22. S. Thomas, S. Ganapathy, A. Jansen, and H. Hermansky, "Data-driven posterior features for low resource speech recognition applications," in Proc. Interspeech, 2012
23. F. Grezl, M. Karafiat, and M. Janda, "Study of probabilistic and bottle-neck features in multilingual environment," in Proc. ASRU, 2011
24. S. Thomas, S. Ganapathy, and H. Hermansky, "Multilingual MLP features for low-resource LVCSR systems," in Proc. ICASSP, 2012
25. S. Sivadas and H. Hermansky, "On use of task independent training data in tandem feature extraction," in Proc. ICASSP, 2004
26. A. Stolcke, F. Grezl, M.-Y. Hwang, X Lei, N. Morgan, and D. Vergyri, "Cross-domain and cross-language portability of acoustic features estimated by multilayer perceptrons," in Proc. ICASSP, 2006
27. V.-B. Le, L. Lamel, and J.-L. Gauvain, "Multi-style MLP features for BN transcription," in Proc. ICASSP, 2010, pp. 4866-4869
28. J. Pinto, M. Magimai-Doss, and H. Bourlard, "MLP based hierachical system for task adaptation in ASR," in Proc. ASRU, 2009
29. P.J. Bell, M.J.F. Gales, P. Lanchantin, X. Liu, Y. Long, S. Renals, P. Swietojanski, and P.C. Woodland, "Transcription of multi-genre media archives using out-of-domain data," in Proc. IEEE Workshop on Spoken Language Technology, Dec. 2012
30. P. Schwarz, Matejka P., and J. Cernokcy, "Hierarchical structures of neural networks for phoneme recognition," in Proc. ICASSP, 2006
31. J Bergstra, O Breuleux, F Bastien, P Lamblin, R Pascanu, G Desjardins, J Turian, D Warde-Farley, and Y Bengio, "Theano: A CPU and GPU math expression compiler," in Proc. SciPy, 2010
32. M Federico, M. Cettolo, L. Bentivogli, M. Paul, and S. Stuker, "Overview of the IWSLT 2012 evaluation campaign," in Proc. of the International Workshop on Spoken Language Translation, Hong Kong, HK, December 2012
33. M. Cettolo, C. Girardi, and M Federico, "Wit3: Web inventory of transcribed and translated talks," in Proceedings of the 16th Conference of the European Association for Machine Translation (EAMT), Trento, Italy, May 2012, pp. 261-268
34. E. Hasler, P. Bell, A. Ghoshal, B. Haddow, P. Koehn, F. McInnes, S. Renals, and P. Swietojanski, "The UEDIN systems for the IWSLT 2012 evaluation," in Proc. IWSLT, 2012.