Hybrid of wavelet and MFCC features for speaker verification

In this paper Wavelet Based Mel Frequency Cepstral Coefficient (WMFCC) features are proposed for speaker verification. The performance of WMFCC features is evaluated and compared with the performance of Mel Frequency Cepstral Coefficient (MFCC) features. A database of ten Hindi digits of sixteen speakers is used during simulation of results. Gaussian Mixture Models (GMMs) are used for maximum log likelihood calculation during verification. The proposed features have shown an increment of 1.18% in performance over MFCC features for text dependent speaker verification system.