Text dependent and independent speaker recognition using neural responses from the model of the auditory system

The objective of this research is identifying a speaker from its voice regardless of the content. Speaker identification is a process to identify a speaker by voice biometrics. It is also known as voice recognition. In this study, both text dependent and text independent procedure for speaker recognition is proposed using the responses of the model of the auditory system. The robustness of the proposed model is also tested here by training the speech signals with the aid of Gaussian Mixture Model and testing by Probability Density Function. Performance tests conducted using the University Malaya and GRID database corpora have shown that this procedure has faster identification time and greater accuracy compared with traditional approaches, and so it is applicable to real-time, text-dependent and independent speaker identification systems.