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2017 | OriginalPaper | Chapter

Learning Deep Wavelet Networks for Recognition System of Arabic Words

Authors : Amira Bouallégue, Salima Hassairi, Ridha Ejbali, Mourad Zaied

Published in: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Publisher: Springer International Publishing

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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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Metadata
Title
Learning Deep Wavelet Networks for Recognition System of Arabic Words
Authors
Amira Bouallégue
Salima Hassairi
Ridha Ejbali
Mourad Zaied
Copyright Year
2017
Book
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Print ISBN: 978-3-319-47363-5

Electronic ISBN: 978-3-319-47364-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47364-2_48

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