This paper presents 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 (MFCC) 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%.
A. Vaishnavi, B Raju, C., Prathiksha, G., L Reddy, H., and Dr. Santhosh Kumar C., “Comparison of Two Speaker Recognition Systems”, International Journal of Engineering and Advanced Technology (IJEAT), vol. 3, 2014.