Speaker Identification Using Cepstral Based Features and Discrete Hidden Markov Model

This paper presents a speaker identification system using cepstral based speech features with discrete hidden Markov model (DHMM). The speaker features represented by the speech signal are potentially characterized by the cepstral coefficients. The commonly used cepstral based features; mel-frequency cepstral coefficient (MFCC), linear predictive cepstral coefficient (LPCC) and real cepstral coefficient (RCC) are employed with DHMM in the speaker identification system. The performances of the proposed method are compared with respect to each of the three feature spaces. The experimental results show that the identification accuracy with MFCC is superior to both of LPCC and RCC.