Wavelet entropy and neural network for text-independent speaker identification

In the present study, the techniques of wavelet transform (WT) and neural network were developed for speech based text-independent speaker identification. The first five formants in conjunction with the Shannon entropy of wavelet packet (WP) upon level four features extraction method was developed. Thirty-five features were fed to feed-forward backpropagation neural networks (FFPBNN) for classification. The functions of features extraction and classification are performed using the wavelet packet and formants neural networks (WPFNN) expert system. The declared results show that the proposed method can make an effectual analysis with average identification rates reaching 91.09. Two published methods were investigated for comparison. The best recognition rate selection obtained was for WPFNN. Discrete wavelet transform (DWT) was studied to improve the system robustness against the noise of −2 dB.