Text dependent speaker recognition using shifted MFCC

In the past decade, interest in using biometric technologies for person authentication in security systems has grown rapidly. Voice is one of the most promising and mature biometric modalities for secured access control. In this paper, we present a novel approach to recognize/identify speakers by including a new set of features and using Gaussian mixture models (GMMs). In this research, the concept of shifted MFCC is introduced so as to incorporate accent information in the recognition algorithm. The algorithm was evaluated using TIDIGIT dataset and the results showed improvements over the performance of our previous work [1].