IMPROVEMENT OF THE TEXT DEPENDENT SPEAKER IDENTIFICATION SYSTEM USING DISCRETE MMM WITH CEPSTRAL BASED FEATURES

Abstract

In this paper, an improved strategy for automated text based speaker identification scheme has been proposed. The identification process incorporates the Hidden Markov Model technique. After preprocessing the speech, HMM is used in the learning and identification. Features are extracted by different techniques such as RCC, MFCC, ÄMFCC, ÄÄMFCC, LPC and LPCC which is almost different in each case. The highest identification rate of 93% has been achieved in the close set text dependent speaker identification system.