Denoising deep neural networks based voice activity detection

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

Volumn , Issue , 2013, Pages 853-857

  Zhang, Xiao Lei ^a   Wu, Ji ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Deep learning; denoising deep neural networks; voice activity detection

Indexed keywords

CLEAN SPEECH; CROSS ENTROPY; DEEP LEARNING; DEEP NEURAL NETWORKS; MULTIPLE FEATURES; NOISY SPEECH SIGNALS; STATE-OF-THE-ART PERFORMANCE; VOICE ACTIVITY DETECTION;

BACKPROPAGATION ALGORITHMS; SIGNAL PROCESSING; SPEECH RECOGNITION;

NEURAL NETWORKS;

EID: 84889263385     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6637769     Document Type: Conference Paper

References (26)

1. D. Yu and L. Deng, "Deep-structured hidden conditional random fields for phonetic recognition," in Proc. IN-TERSPEECH, 2010, pp. 2986-2989.
2. G. Dahl, D. Yu, L. Deng, and A. Acero, "Contextdependent pre-trained deep neural networks for large vocabulary speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 1, pp. 30-42, 2012.
3. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, et al., "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Process. Mag., vol. 29, no. 11, pp. 2-17, 2012.
4. K. Hana and D. L. Wang, "A classification based approach to speech segregation," The Journal of the Acoustical Society of America, vol. 99, pp. 1-34, 2012.
5. D. L. Wang, "The time dimension for scene analysis," IEEE Trans. Neural Netw., vol. 16, no. 6, pp. 1401-1426, 2005.
6. D. L. Wang and G. J. Brown, Computational auditory scene analysis: principles, algorithms and applications, Wiley-IEEE Press, 2006.
7. Y. X. Wang, K. Han, and D. L. Wang, "Exploring monaural features for classification-based speech segregation," IEEE Trans. Audio, Speech, Lang. Process., vol. 1, no. 99, pp. 1-10, 2012.
8. Y. X.Wang and D. L.Wang, "Cocktail party processing via structured prediction," in Proc. Adv. Neural Inform. Process. Syst., 2012, pp. 1-8.
9. Y. X. Wang and D. L. Wang, "Towards scaling up classification-based speech separation," IEEE Trans. Audio, Speech, Lang. Process., vol. PP, no. 99, pp. 1-23, 2013.
10. S. I. Kang, Q. H. Jo, and J. H. Chang, "Discriminative weight training for a statistical model-based voice activity detection," IEEE Signal Process. Lett., vol. 15, pp. 170-173, 2008.
11. J. W. Shin, J. H. Chang, and N. S. Kim, "Voice activity detection based on statistical models and machine learning approaches," Computer Speech & Language, vol. 24, no. 3, pp. 515-530, 2010.
12. T. Yu and J. H. L. Hansen, "Discriminative training for multiple observation likelihood ratio based voice activity detection," IEEE Signal Process. Lett., vol. 17, no. 11, pp. 897-900, 2010.
13. J. Wu and X. L. Zhang, "Maximum margin clustering based statistical VAD with multiple observation compound feature," IEEE Signal Process. Lett., vol. 18, no. 5, pp. 283-286, 2011.
14. J. Wu and X. L. Zhang, "Efficient multiple kernel support vector machine based voice activity detection," IEEE Signal Process. Lett., vol. 18, no. 8, pp. 466-499, 2011.
15. X. L. Zhang and J. Wu, "Linearithmic time sparse and convex maximum margin clustering," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 1, no. 99, pp. 1-24, 2012.
16. Y. Suh and H. Kim, "Multiple acoustic model-based discriminative likelihood ratio weighting for voice activity detection," IEEE Signal Process. Lett., vol. 19, no. 8, pp. 507-510, 2012.
17. X. L. Zhang and J. Wu, "Deep belief networks based voice activity detection," IEEE Trans. Audio, Speech, Lang. Process., vol. 21, no. 4, pp. 3371-3408, 2013.
18. G.E. Hinton and R.R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, 2006.
19. M. A. Carreira-Perpinan and G. E. Hinton, "On contrastive divergence learning," in Proc. Int. Conf. Artif. Intell. Stat., 2005, pp. 17-25.
20. P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol, "Extracting and composing robust features with denoising autoencoders," in Proc. 25th Int. Conf. Mach. Learn., 2008, pp. 1096-1103.
21. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn. Res., vol. 11, pp. 3371-3408, 2010.
22. Y. Ephraim and D. Malah, "Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator," IEEE Trans. Acoustic, Speech, Signal Process., vol. 32, no. 6, pp. 1109-1121, 1984.
23. J. Sohn, N. S. Kim, and W. Sung, "A statistical modelbased voice activity detection," IEEE Signal Process. Lett., vol. 6, no. 1, pp. 1-3, 1999.
24. Israel Cohen, "Noise spectrum estimation in adverse environments: Improved minima controlled recursive averaging," IEEE Trans. Speech, Audio Process., vol. 11, no. 5, pp. 466-475, 2003.
25. J. Ramírez, J. C. Segura, C. Benítez, L. García, and A. Rubio, "Statistical voice activity detection using a multiple observation likelihood ratio test," IEEE Signal Process. Lett., vol. 12, no. 10, pp. 689-692, 2005.
26. Z. Xu, R. Jin, H. Yang, I. King, and M. R. Lyu, "Simple and efficient multiple kernel learning by group lasso," in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 1175-1182.