In this work, the Mel frequency Cepstrum Coefficient

(MFCC) feature has been used for designing a text

dependent speaker identification system. The extracted

speech features (MFCC’s) of a speaker are quantized to a

number of centroids using vector quantization algorithm.

These centroids constitute the codebook of that speaker.

MFCC’s are calculated in training phase and again in testing

phase. Speakers uttered same words once in a training

session and once in a testing session later. The Euclidean

distance between the MFCC’s of each speaker in training

phase to the centroids of individual speaker in testing phase

is measured and the speaker is identified according to the

minimum Euclidean distance. The code is developed in the

MATLAB environment and performs the identification

satisfactorily.