BENGALI TEXT DEPENDENT SPEAKER IDENTIFICATION USING MELFREQUENCY CEPSTRUM COEFFICIENT AND VECTOR QUANTIZATION
Ruhul Amin · 2004
In this work, the well-known Melfrequency Cepstrum Coefficient (MFCC) feature has been used for designing a Bengali text dependent speaker identification system. The extracted speech features (MFCC’s) of a speaker are quantized to a number of centroids using LBG algorithm. These centroids constitute the codebook of that speaker. MFCC's are calculated in training phase and again in testing phase. Speakers uttered different Bengali words once in a training session and once in a testing session later. The quantization distance between the 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 quantization distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.