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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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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