MareText Independent Speaker Identification based on K-mean Algorithm

This paper proposes a text-independent speaker identification system based on Mel Frequency Cepstral Coefficients as a feature extraction and Vector Quantization technique that would minimize the data required for processing. The correlation between the identification success rate and the various parameters of the system including the feature extraction tools and the data minimization technique will be examined. Extracted features of a speaker are quantized by a number of centroids and the K-mean algorithm has been integrated into the proposed speaker identification system. Such centroids constitute the codebook of that speaker. MFCC are calculated in both training and testing phases. To calculate these MFCC speakers uttered different words, once in a training session and once in a testing one. The speakers were identified according to the minimum quantization distance which was calculated between the centroids of each speaker in the training phase and the MFCC of individual speakers in the testing phase. Analysis was carried out to identify parameter values that could be used to improve the performance of the system. The experimental results illustrate the efficiency of the proposed method under several conditions