Variable-component deep neural network for robust speech recognition

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2014, Pages 2719-2723

  Zhao, Rui ^a   Li, Jinyu ^b   Gong, Yifan ^b  

^a MICROSOFT RESEARCH ASIA   (China)

^b MICROSOFT   (United States)

Author keywords

Robust speech recognition; Variable component; Variable output; Variable parameter

Indexed keywords

COST REDUCTION; HIDDEN MARKOV MODELS; SIGNAL TO NOISE RATIO; SPEECH COMMUNICATION;

DEEP NEURAL NETWORKS; ENVIRONMENTAL SIGNALS; POLYNOMIAL FUNCTIONS; ROBUST SPEECH RECOGNITION; VARIABLE COMPONENT; VARIABLE OUTPUT; VARIABLE PARAMETERS; WORD ERROR RATE REDUCTIONS;

SPEECH RECOGNITION;

EID: 84910072102     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. D. Yu, L. Deng, and G. Dahl, "Roles of pretraining and finetuning in context-dependent DBN-HMMs for real-world speech recognition, " in Proc. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2010.
2. T. N. Sainath, B. Kingsbury, B. Ramabhadran, P. Fousek, P. Novak, and A. Mohamed, "Making deep belief networks effective for large vocabulary continuous speech recognition, " in Proc. Workshop on Automatic Speech Recognition and Understanding, pp. 30-35, 2011.
3. G. E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, " IEEE Trans. on Audio, Speech and Language Processing, vol. 20, no. 1, pp. 30-42, 2012.
4. N. Jaitly, P. Nguyen, and V. Vanhoucke, "Application of pretrained deep neural networks to large vocabulary speech recognition", in Proc. Interspeech, 2012.
5. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, " IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012.
6. L. Deng, J. Li, J. -T. Huang et al. "Recent advances in deep learning for speech research at Microsoft, " in Proc. ICASSP, 2013.
7. J. Li, L. Deng, Y. Gong, and R. Haeb-Umbach, "An overview of noise-robust automatic speech recognition, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014.
8. M. L. Seltzer, D. Yu, and Y. Wang, "An investigation of deep neural networks for noise robust speech recognition, " in Proc. ICASSP, pp. 7398-7402, 2013.
9. B. Li and K. C. Sim, "Noise adaptive front-end normalization based on vector Taylor series for deep neural networks in robust speech recognition, " in Proc. ICASSP, pp. 7408-7412, 2013.
10. B. Li, Y. Tsao, and K. C. Sim, "An investigation of spectral restoration algorithms for deep neural networks based noise robust speech recognition, " in Proc. Interspeech, pp. 3002-3006, 2013.
11. M. Delcroix, Y. Kubo, T. Nakatani, and A. Nakamura, "Is speech enhancement pre-processing still relevant when using deep neural networks for acoustic modeling, " in Proc. Interspeech, pp. 2992-2996, 2013.
12. X. Cui and Y. Gong, "A study of variable-parameter Gaussian mixture hidden Markov modeling for noisy speech recognition, " IEEE Trans. on Audio, Speech and Language Processing, vol. 15, no. 4, pp. 1366-1376, 2007.
13. D. Yu, L. Deng, Y. Gong, and A. Acero. "A novel framework and training algorithm for variable-parameter hidden Markov models, " IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 7, 1348-1360, 2009.
14. N. Cheng, X. Liu, and L. Wang, "Generalized variable parameter HMMs for noise robust speech recognition, " in Proc. Interspeech, pp. 481-484, 2011.
15. M. Radenen and T. Artieres, "Contextual hidden Markov models, " in Proc. ICASSP, pp. 2113-2116, 2012.
16. Y. Li, X. Liu, and L. Wang, "Feature space generalized variable parameter HMMs for noise robust recognition, " in Proc. Interspeech, pp. 2968-2972, 2013.
17. R. Lippmann, E. Martin, and D. Paul, "Multi-style training for robust isolated-word speech recognition, " in Proc. IEEE Int. Conf. Acoustic Speech Signal Processing, pp. 705-708, 1987.
18. M. Blanchet, J. Boudy and P. Lockwood, "Environment adaptation for speech recognition in noise, " in Proc. EUSIPCO, 1992, pp. 391-394.
19. Y. M. Cheng et al., "A robust front-end algorithm for distributed speech recognition, " in Proc. EUR. Conf. Speech Communication and Technology, 2001.
20. N. Parihar and J. Picone, "Aurora working group: DSR front end LVCSR evaluation AU/384/02, " Tech. Rep., Institute for Signal and Information Processing, Mississippi State Univ., 2002.
21. J. Li, R. Zhao, J.-T. Huang, and Y. Gong, "Learning small-size DNN with output-distribution-based criteria, " in Proc. Interspeech, 2014.
22. J. Li, J.-T. Huang, and Y. Gong, "Factorized adaptation for deep neural network, " Proc. ICASSP, 2014.