The use of wavelet entropy in conjuction with neural network for Arabic vowels recognition

Authors:
W. Al-Sawalmeh;K. Daqrouq;O. Daoud
Affiliations:
Department of Communication Engineering, Al-Hussein Bin Talal University, Ma'an, Jordan;Comp. & Electrical Eng. Dept, King Abdulaziz Univ., Jeddah, Saudi Arabia;Department of Communication and Electronics Engineering, Philadelphia University, Amman, Jordan
Venue:
WSEAS Transactions on Signal Processing
Year:
2011

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Abstract

In this research paper, Arabic vowels recognition system using very promising techniques; wavelet packet transform (WT) with entropy and neural network was presented. Trying to enhance the recognition process, three types of entropies were applied for the wavelet packet (WP) of the speech signals. Moreover, different levels of WP were used in order to enhance the efficiency of the proposed work until level 7. To classify among the feature vectors; a probabilistic neural network (PNN) were used. A MATLAB program was used to build the model of the proposed work to show the powerfulness of 96.77% identification rate. This is due to that the functions of features extraction and classifications are performed using the entropy, wavelet packet and neural networks.