Text Independent Speaker Identification System

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Abeer M. Abu-Hantash
Alaa Tayseer Spaih
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            In this report, a text-independent speaker identification system is introduced. The well knownMel Frequency Cepstral coefficients {MFCCs} have been used for feature extraction and vector quantization technique is used to minimize the amount of data to be handled. The extracted speech features {MFCCs} of a speaker is quantized to a number of centroids using the K-mean algorithm. And these centroids constitute the codebook of that speaker. MFCCs are calculated in both training and testing phase. Speakers uttered different words, once in a training session and once in a testing session later. The speaker is identified according to the minimum quantization distance which is calculated between the centroids of each speaker in training phase and the MFCCs of individual speaker in testing phase. The code is developed in Matlab environment and performs the identification satisfactorily.