SPEAKER IDENTIFICATION SYSTEM USING CLOSE SET

Shweta Bansal, Ankur Hooda, Anima

Abstract: The present paper describes experiments conducted to evaluate the performance of speaker recognition. The experiments conducted using Neural Network shows that the complexity of speaker recognition increases when the numbers of speakers to be identified are large in numbers in the text independent situation. In the first experiment error rate was zero for 10 speaker’s classification and performance was good. By increasing the number of speakers the error rate increased, classification and performance were poor. After 25 speakers, the error rate was very high. For 100 speaker’s classification MATLAB NN tool did not support for display the confusion matrix. To overcome this problem the second experiment has been done. In this experiment a close set of 10 groups of 100 speakers (each group of 10 speakers) in terms of cell array in MATLAB has been defined and we observed that the best result of speaker identification was 100% in 20 continuous features of speaker’s voice, but it increased time complexity. In the third experiment speaker’s dialect and regions were also identified and classification performance was 100% at 97 epochs, validation performance was 0.0035046 at 91 epochs and the error rate was zero has found

Keywords: text dependent, text independent, speaker identification, Neural Network, close set, MFCC, Speaker identification, close set.

DOI: https://doi.org/10.15623/ijret.2012.0103038