Abstract
In this paper, we propose a new method of learning for speech signal. This technique is based on the deep learning and the wavelet network theories. The goal of our approach is to construct a deep wavelet network (DWN) using a series of Stacked Wavelet Auto-Encoders. The DWN is devoted to the classification of one class compared to other classes of the dataset. The Mel-Frequency Cepstral Coefficients (MFCC) is chosen to select speech features. Finally, the experimental test is performed on a prepared corpus of Arabic words.
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References
Pati, Y.C., Krishnaprasad, P.S.: Discrete affine wavelet transforms for analysis of feed-forward neural networks. In: Proceedings of the 1990 Conference on Advances in Neural Information Processing Systems, Denver, Colorado, United States, vol. 3, pp. 743–749 (1991)
Zhang, Q., Benveniste, A.: Wavelet networks. IEEE Trans. Neural Netw. 3(6), 889–898 (1992)
Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)
Hinton, G., Deng, L., Dong, Y., Dahl, G.E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition. In: The Shared Views of Four Research Groups IEEE Signal Processing Magazine, November 2012
Liou, C.-Y., Cheng, W.-C., Liou, J.-W., Liou, D.-R.: Autoencoder for words. Neurocomputing 139, 84–96 (2014)
Reducing the Dimensionality of Data with Neural Networks (Science, Hinton and Salakhutdinov), 28 July 2006
Shrawankar, U., Thakare, V.: Techniques for feature extraction in speech recognition system: acomparative study. Int. J. Comput. Appl. Eng. Technol. Sci. (2010)
Joseph, W.: PICONE: signal modeling techniques in speech recognition. Proc. IEEE 81(9), 1215–1247 (1993)
Zaied, M., Said, S., Jemai, O., ben Amar, C.: A novel approach for face recognition based on fast learning algorithm and wavelet network theory. Int. J. Wavelets Multiresolut. Inf. Process. 9(6), 923–945 (2011)
Jemai, O., Zaied, M., Amar, C.B., Alimi, A.M.: FBWN: an architecture of fast beta wavelet networks for image classification. Int. Joint Conf. Neural Netw. July 2010
ElAdel, A., Ejbali, R., Zaied, M., Amar, C.B.: A new system for image retrieval using beta wavelet network fordescriptors extraction and fuzzy decision support. In: International Conference of Soft Computing and Pattern Recognition, pp. 232–236 (2014)
LeCun, Y.: Learning invariant feature hierarchies. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7583, pp. 496–505. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33863-2_51
Jemai, O., Ejbali, R., Zaied, M., Amar, C.B.: A speech recognition system based on hybrid wavelet network including a fuzzy decision support system. In: Proceedings of SPIE - The International Society for Optical Engineering, February 2015
Hassairi, S., Ejbali, R., Zaied, M.: Supervised image classification using deep convolutional wavelets network. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (2015)
Boudraa, M., Boudraa, B.: Twenty list of ten arabic sentences for assessment. ACUSTICA Acta Acoustica 86(43.71), 870–882 (1998)
Ejbali, R., Zaied, M., Ben Amar, C.: Intelligent approach to train wavelet networks for recognition system of arabic words. In: KDIR International Joint Conference on Knowledge Discovery and Information Retrieval, Valencia, Spain, pp. 518–522, 25–28 October 2010
Ejbali, R., Zaied, M., Amar, C.B.: Multi-input multi-output beta wavelet network: modeling of acoustic units for speech recognition. Int. J. Adv. Comput. Sci. Appl. 3(3) (2012)
Ejbali, R., Zaied, M., Amar, C.B.: Wavelet network for recognition system of Arabic word. Int. J. Speech Technol. 13(3), 163 (2010)
Acknowledgment
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Bouallégue, A., Hassairi, S., Ejbali, R., Zaied, M. (2017). Learning Deep Wavelet Networks for Recognition System of Arabic Words. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_48
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DOI: https://doi.org/10.1007/978-3-319-47364-2_48
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