An Efficient Text Dependent Speaker Recognition using Fusion of MFCC and SBC

In this paper an efficient approach for the recognition of a speaker based on text dependent speech is presented. Speaker Recognition/Verification system suffers with wide variety of problems. In the proposed approach, the features are extracted using two methods such as Mel Frequency Cepstral Coefficients and wavelet subband coefficients, and then these futures are fused through concatenation to give optimum performance. Those concatenated feature set are more reliable to discriminate an imposter from the genuine. Those concatenated features are classified using support vector machine classifier. Performance of the proposed approach is validated on a self generated corpus of size 300 samples of 20 individual. The proposed method outperforms other existing methods.