Real-time speaker identification system using cepstral features

Abstract

Real-time speaker identification (SI) system is the application of Biometric system where the voice samples are collected in real-time. Due to that contamination of noises in speaker samples are the natural scenario. In this work, we tried to increase the accuracy of real-time SI system. We analysed the SI system by using different feature extraction methods with GMM-ML classifier. We found that MFCC feature extraction method is the best one among other cepstral features in real-time SI system also. We used different scale based feature extraction methods for the evaluation of SI system. We used the database for SI system created in real-time.