Wavelet Packet and Percent of Energy Distribution with Neural Networks Based Gender Identification System

This research presents the study of gender identification for security systems based on the energy of speaker utterances. The proposed system consisted of a combination of signal pre-process, feature extraction using Wavelet Packet Transform (WPT) and gender identification using artificial neural network. In the signal pre-process, the amplitude of utterances was normalized for preventing an error estimation caused by speakers change in volume. 128 features fed to Feed Forward Backpropagation Neural Networks (FFPBNN) for classification. The functions of features extraction and classification are performed using the Wavelet Packet and Percent of Energy Distribution and Neural Networks (WPENN) expert system. The declared results showed that the proposed method can make an effectual analysis with average identification rates reached 91.09. Two published methods were investigated for comparison. The best recognition rate selection obtained was for WPENN. The proposed method can offer a significant computational advantage by reducing the dimensionality of the WP coefficients by means of percent of energy distribution. Discrete Wavelet Transform (DWT) was studied to improve the system robustness against the noise of -2 dB. DWT approximation Sub-signal through several levels instead of original imposter had good performance on Additive White Gaussian Noise (AWGN) facing, particularly upon level 4.