Learning Deep Wavelet Networks for Recognition System of Arabic Words
Amira Bouallégue, Salima Hassairi, Ridha Ejbali, Mourad Zaied · Advances in intelligent systems and computing · 2016
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.