Comparison of Two Speaker Recognition Systems
In this paper, the authors present a comparison between two speaker recognition systems. One system uses 30 Shannon entropy values extracted from a four level wavelet packet decomposition method in addition to the first three formant frequencies as features and a cascaded feed forward back propagation neural network is used as classifier. The second system uses Mel Frequency Cepstral Coefficients (MFCCs) as features and a Support Vector Machine (SVM) as classifier. Results suggest that wavelet based system has better performance than the classic MFCCs with an efficiency of 89.56%.