Convolutional deep maxout networks for phone recognition

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

Volumn , Issue , 2014, Pages 1078-1082

  Tóth, László ^a  

^a UNIVERSITY OF SZEGED   (Hungary)

Author keywords

Convolutional neural networks; Deep neural networks; Maxout networks; TIMIT

Indexed keywords

CHEMICAL ACTIVATION; CONVOLUTION; ELECTRIC RECTIFIERS; NEURAL NETWORKS; NEURONS; SPEECH COMMUNICATION; TELEPHONE CIRCUITS; TELEPHONE SETS;

ACTIVATION FUNCTIONS; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; DEEP NEURAL NETWORKS; NETWORK CONFIGURATION; NEURAL ACTIVATION; RELATIVE ERROR RATES; TIMIT;

SPEECH RECOGNITION;

EID: 84910069623     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. O. Abdel-Hamid, A. Mohamed, H. Jiang, and G. Penn, "Applying convolutional neural network concepts to hybrid NN-HMM model for speech recognition, " in Proc. ICASSP, 2012, pp. 4277 - 4280.
2. L. Deng, O. Abdel-Hamid, and D. Yu, "A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion, " in Proc. ICASSP, 2013, pp. 6669 - 6673.
3. O. Abdel-Hamid, L. Deng, and D. Yu, "Exploring convolutional neural network structures and optimization techniques for speech recognition, " in Proc. Interspeech, 2013, pp. 3366 - 3370.
4. T. N. Sainath, A. Mohamed, B. Kingsbury, and B. Ramabhadran, "Deep convolutional neural networks for LVCSR, " in Proc. ICASSP, 2013, pp. 8614-8618.
5. T. N. Sainath, B. Kingsbury, A. Mohamed, and B. Ramabhadran, et al. "Improvements to deep convolutional neural networks for LVCSR, " in Proc. ASRU, 2013, pp. 315-320.
6. L. Tóth, "Combining time- And frequency-domain convolution in convolutional neural network-based phone recognition, " in Proc. ICASSP. 2014, accepted, in print.
7. K. Veselý, M. Karafiát, and F. Grézl, "Convolutive bottleneck network features for LVCSR, " in Proc. ASRU, 2011, pp. 42 - 47.
8. L. Tóth, "Convolutional deep rectifier neural nets for phone recognition, " in Proc. Interspeech, 2013, pp. 1722-1726.
9. I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, "Maxout networks, " in Proc. ICML, 2013, pp. 1319- 1327.
10. M. Cai, Y. Shi, and J. Liu, "Deep maxout neural networks for speech recognition, " in Proc. ASRU, 2013, pp. 291-296.
11. Y. Miao, F. Metze, and S. Rawat, "Deep maxout networks for low-resource speech recognition, " in Proc. ASRU, 2013, pp. 398- 403.
12. P. Swietojanski, J. Li, and J. T. Huang, "Investigation of maxout networks for speech recognition, " in Proc. ICASSP. 2014, accepted, in print.
13. X. Zhang, J. Trmal, D. Povey, and S. Khudanpur, "Improving deep neural network acoustic models using generalized maxout networks, " in Proc. ICASSP. 2014, accepted, in print.
14. M. Cai, Y. Shi, and J. Liu, "Stochastic pooling maxout networks for low-resource speech recognition, " in Proc. ICASSP. 2014, accepted, in print.
15. H. Ketabdar and H. Bourlard, "Enhanced phone posteriors for improving speech recognition systems, " IEEE Trans. ASLP, vol. 18, no. 6, pp. 1094-1106, 2010.
16. J. Pinto et al., "Analysis of MLP based hierarchical phoneme posterior probability estimator, " IEEE Trans. ASLP, vol. 19, no. 2, pp. 225-241, 2010.
17. X. Glorot, A. Bordes, and Y. Bengio, "Deep sparse rectifier neural networks, " in Proc. AISTATS, 2011.
18. G. E. Dahl, T. N. Sainath, and G. E. Hinton, "Improving deep neural networks for LVCSR using rectified linear units and dropout, " in Proc. ICASSP, 2013, pp. 8609-8613.
19. M. D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q. V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, and G. E. Hinton, "On rectified linear units for speech processing, " in Proc. ICASSP, 2013, pp. 3517-3521.
20. L. Tóth, "Phone recognition with deep sparse rectifier neural networks, " in Proc. ICASSP, 2013, pp. 6985-6989.
21. A. L. Maas, A. Y. Hannun, and A. Y. Ng, "Rectifier nonlinearities improve neural network acoustic models, " in Proc. ICML, 2013.
22. A. Mohamed, G. E. Dahl, and G. Hinton, "Acoustic modeling using deep belief networks, " IEEE Trans. ASLP, vol. 20, no. 1, pp. 14-22, 2012.
23. F. Seide, G. Li, L. Chen, and D. Yu, "Feature engineering in context-dependent deep neural networks for conversational speech transcription, " in Proc. ASRU, 2011, pp. 24-29.
24. G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Improving neural networks by preventing coadaptation of feature detectors, " CoRR, vol. abs/1207.0580, 2012.