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%.

Provided by: International Journal of Engineering and Advanced Technology (IJEAT) Topic: Hardware Date Added: Apr 2014 Format: PDF

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