Learning Deep Wavelet Networks for Recognition System of Arabic Words

• Amira Bouallégue ^[1] ; Salima Hassairi ^[2] ; Ridha Ejbali ^[2] ; Mourad Zaied ^[2]
  1. [1] University of Gabès

     University of Gabès

     Túnez

     
  2. [2] REsearch Group in Intelligent Machines (Sfax, Tunis, Tunisia)
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 498-507
• Idioma: inglés
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• Resumen

  • 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.