Ensemble of deep neural networks using acoustic environment classification for statistical model-based voice activity detection

Computer Speech and Language

Volumn 38, Issue , 2016, Pages 1-12

  Hwang, Inyoung ^a   Park, Hyung Min ^b   Chang, Joon Hyuk ^a  

^a Hanyang University   (South Korea)

^b Sogang University   (South Korea)

Author keywords

Acoustic environment classification; Deep neural network; Ensemble; Statistical model; Voice activity detection

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; PROBABILITY; STATISTICS; SUPPORT VECTOR MACHINES;

ACOUSTIC ENVIRONMENT; DEEP NEURAL NETWORKS; ENSEMBLE; STATISTICAL MODELING; VOICE ACTIVITY DETECTION;

SPEECH RECOGNITION;

EID: 84951099137     PISSN: 08852308     EISSN: 10958363     Source Type: Journal    
DOI: 10.1016/j.csl.2015.11.003     Document Type: Article

References (23)

1. C. Bishop Neural Networks for Pattern Recognition 1995 Oxford Press
2. J.-H. Chang, and N.S. Kim Speech enhancement: new approaches to soft decision IEICE Trans. Syst. Inf. E84-D 9 2001 1231 1240
3. J.-H. Choi, and J.-H. Chang On using environment classification for statistical model-based speech enhancement Speech Commun. 54 2012 477 490
4. Y. Ephraim, and D. Malah Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator IEEE Trans. Acoust. Speech Signal Process. 32 6 1984 1109 1121
5. J.S. Garofolo, L.F. Lamel, W.M. Fisher, J.G. Fiscus, D.S. Pallett, N.L. Dahlgren, and et al. TIMIT acoustic phonetic continuous speech corpus Linguistic Data Consortium, Philadelphia (PA, USA) 1993 (Corpus LDC93S1)
6. G. Hinton, and R. Salakhutdinov Reducing the dimensionality of data with neural networks Science 313 5786 2006 504 507
7. G. Hinton, S. Osindero, and T. Teh A faster learning algorithm for deep belief nets Neural Comput. 18 7 2006 1527 1554
8. G. Hinton, L. Deng, 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 IEEE Signal Process. Mag. 29 6 2012 82 97
9. H. Hirsch, and D. Pearce The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions Proc. ASR 2000
10. Q.-H. Jo, J.-H. Chang, J. Shin, and N.S. Kim Statistical model-based voice activity detection using support vector machine IET Signal Process. 3 3 2009 205 210
11. 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. 15 2008 170 173
12. H. Lee, C. Ekanadham, and A. Ng Sparse deep belief net model for visual area v2 Proc. NIPS 2007
13. A. Mohamed, G. Dahl, and G. Hinton Deep belief networks for phone recognition Proc. NIPS 2009
14. A. Mohamed, G. Dahl, and G. Hinton Acoustic modeling using deep belief networks IEEE Trans. Audio Speech Lang. Process. 20 1 2012 14 22
15. J. Platt Probabilistic outputs for support vector machines and comparison to regularized likelihood methods Advances in Large Margin Classifiers 2000 Cambridge Press
16. N. Ryant, M. Liberman, and J. Yuan Speech activity detection on YouTube using deep neural networks Proc. Interspeech 2013
17. A. Sangwan, N. Krishnamurthy, and H. Hansen Environmentally aware voice activity detector Proc. Interspeech 2007
18. J. Sohn, N.S. Kim, and W. Sung A statistical model-based voice activity detection IEEE Signal Process. Lett. 1 6 1999 1 3
19. A. Varga, and H. Steeneken Assessment for automatic speech recognition, II-NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems Speech Commum. 12 1993 247 251
20. B. Xia, and C. Bao Wiener filtering based speech enhancement with weighted denoising auto-encoder and noise classification Speech Commun. 60 2014 13 29
21. T. Yu, and J. Hansen Discriminative training for multiple observation likelihood ratio based voice activity detection IEEE Signal Process. Lett. 17 11 2010 897 900
22. X.-L. Zhang, and J. Wu Denoising deep neural networks based voice activity detection Proc. ICASSP 2013
23. X.-L. Zhang, and J. Wu Deep belief networks based voice activity detection IEEE Trans. Audio Speech Lang. Process. 21 4 2013 3371 3408