The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter Values. These Optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the Optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition - short time Fourier transform, wavelet decomposition - Born-Jordan time-frequency representation, wavelet decomposition - Choi-Williams time-frequency representation, wavelet decomposition - Margenau-Hill time-frequency representation, wavelet decomposition - Wigner-Ville time-frequency representation. wavelet decomposition - Page time-frequency representation, wavelet decomposition - Zhao-Atlas-Marks time-frequency representation, The wavelet layer is used for Optimum feature extraction in the time-frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification. (C) 2009 Published by Elsevier Ltd.