Speaker Identification Based on Physical Variation of Speech Signal

Speaker identification for the speech signal processing request, determining the speaker is a challenge due to physical variation. This paper emphasizes a new algorithm based on acoustic feature analysis of text-dependent speech. In this proposed method text-dependent speech changed by ten physical variation methods. Acoustic feature of all types of voice is calculated by its arithmetical correlation coefficients and mean value. The audio characteristic is calculated with Mel-frequency cepstrum coefficient (MFCC), its derivatives and double derivatives. An acoustic characteristic is analysed by using normal voice and changed voice by different speakers, the mixed data used for test and training purpose. Passing all the training and test data through the various classifiers based on identification system. Speaker identification efficiency results are calculated from the different classifier.