Improved text-independent speaker identification system for real time applications

Abstract

Speaker identification identifies the speaker among a set of users by matching against a set of voiceprints. In speaker identification, the identification time depends on the number of feature vectors, their dimensionality and the number of speakers. In this paper, text independent speaker identification model is developed by taking in MFCCs with VQ to obtain pressed feature vectors without losing much information, and the numbers of speakers are reduced in the test by gender detection algorithm. Gaussian Mixture Model (GMM) is used a modeling technique. Results show that proposed approach always yields better improvements in accuracy and brings almost 50% reduces in time processing.