Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks

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

Volumn , Issue , 2013, Pages 1766-1770

  Palaz, Dimitri ^a,b   Collobert, Ronan ^a   Magimai Doss, Mathew ^a  

^a IDIAP RESEARCH INSTITUTE   (Switzerland)

^b EPFL   (Switzerland)

Author keywords

Artificial neu ral networks; Automatic speech recognition; Convolutional neural networks; Data driven feature extraction; Phonemes

Indexed keywords

FEATURE EXTRACTION; IMAGE PROCESSING; LEARNING SYSTEMS; SPEECH RECOGNITION;

AUTOMATIC SPEECH RECOGNITION; AUTOMATIC SPEECH RECOGNITION SYSTEM; CONDITIONAL PROBABILITIES; CONVOLUTIONAL NEURAL NETWORK; MACHINE LEARNING TECHNIQUES; MULTI LAYER PERCEPTRON; PHONEME CLASSIFICATION; PHONEMES;

NEURAL NETWORKS;

EID: 84906273908     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition, " Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
2. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing (almost) from scratch, " The Journal of Machine Learning Research, vol. 12, pp. 2493-2537, 2011.
3. A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K. Lang, "Phoneme recognition using time-delay neural networks, " Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 37, no. 3, pp. 328 -339, mar 1989.
4. Y. LeCun, "Generalization and network design strategies, " in Connectionism in Perspective, R. Pfeifer, Z. Schreter, F. Fogelman, and L. Steels, Eds. Zurich, Switzerland: Elsevier, 1989.
5. A. Poritz, "Linear predictive hidden markov models and the speech signal, " in Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '82., vol. 7, May 1982, pp. 1291-1294.
6. Y. Ephraim and W. J. J. Roberts, "Revisiting autoregressive hidden markov modeling of speech signals, " IEEE Signal Processing Letters, vol. 12, no. 2, pp. 166-169, Feb. 2005.
7. B. Mesot and D. Barber, "Switching linear dynamical systems for noise robust speech recognition, " IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 6, pp. 1850-1858, Aug. 2008.
8. H. Sheikhzadeh and L. Deng, "Waveform-based speech recognition using hidden filter models: Parameter selection and sensitivity to power normalization, " Speech and Audio Processing, IEEE Transactions on, vol. 2, no. 1, p. 8089, 1994.
9. J. Yousafzai, Z. Cvetkovic, and P. Sollich, "Tuning support vector machines for robust phoneme classification with acoustic waveforms, " in INTERSPEECH, 2009, pp. 2391-2394.
10. A. Mohamed, G. Dahl, and G. Hinton, "Acoustic modeling using deep belief networks, " Audio, Speech, and Language Processing, IEEE Transactions on, vol. 20, no. 1, pp. 14 -22, jan. 2012.
11. O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, and G. Penn, "Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition, " in Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2012, pp. 4277-4280.
12. G. E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, " Audio, Speech, and Language Processing, IEEE Transactions on, vol. 20, no. 1, p. 3042, 2012.
13. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and T. N. Sainath, "Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, " Signal Processing Magazine, IEEE, vol. 29, no. 6, p. 8297, 2012.
14. Y. LeCun, F. J. Huang, and L. Bottou, "Learning methods for generic object recognition with invariance to pose and lighting, " in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2004, pp. II-97.
15. A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks, " in Advances in Neural Information Processing Systems 25, 2012, pp. 1106-1114.
16. J. Bridle, "Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, " in Neuro-computing: Algorithms, Architectures and Applications, NATO ASI series ed., F. Fogelman Soulie and J. Herault, Eds., 1990, pp. 227-236.
17. L. Bottou, "Stochastic gradient learning in neural networks, " in Proceedings of Neuro-Nmes 91. Nimes, France: EC2, 1991.
18. K. F. Lee and H. W. Hon, "Speaker-independent phone recognition using hidden markov models, " IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 37, no. 11, pp. 1641- 1648, 1989.
19. S. Young, G. Evermann, D. Kershaw, G. Moore, J. Odell, D. Ollason, V. Valtchev, and P. Woodland, "The htk book, " Cambridge University Engineering Department, vol. 3, 2002.
20. N. Morgan and H. Bourlard, "Continuous speech recognition, " Signal Processing Magazine, IEEE, vol. 12, no. 3, pp. 24 -42, May 1995.
21. R. Collobert, K. Kavukcuoglu, and C. Farabet, "Torch7: A matlab-like environment for machine learning, " in BigLearn, NIPS Workshop, 2011.
22. S. Thomas, S. Ganapathy, and H. Hermansky, "Phoneme recognition using spectral envelope and modulation frequency features, " in Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, 2009, pp. 4453-4456.
23. L. Bottou, Y. Bengio, and Y. LeCun, "Global training of document processing systems using graph transformer networks." in In Proc. of Computer Vision and Pattern Recognition. Puerto-Rico., 1997, pp. 490-494.