Speaker recognition using weighted dynamic MFCC based on GMM

In this paper, a new algorithm of feature parameter extraction is proposed for application in speaker recognition system, which combines the traditional MFCC and the dynamic MFCC as a new series of coefficients. According to the statistics analysis of the different contribution by the dynamic MFCC and traditional MFCC, these coefficients are weighted as front-end parameters of the GMM, which would decrease the dimension of the mixed weighted GMM and reduce the computation complexity. The experiments based on the TIMIT and VOA speech database were implemented in MATLAB environment, and the results showed the speaker recognition system with the Weighted Dynamic MFCC could obtain better performance with high recognition rate and low computational complexity.