An improved system for large population text independent Speaker Recognition with short utterances

This paper presents a new text independent speaker recognition system based on new cepstral Coefficients and i-vector based on probabilistic linear discriminant analysis. This system is designed to be used as a biometric authentication system based on the speaker voice. The experiments were performed on speech data taken from a large number of users and consist of 630 speakers from TIMIT database for different training conditions. The efficiency of the proposed system is observed with the increase of Speaker identification Rate. Experimental results show that the system gives higher recognition performance for different numbers of speakers.