A novel method for Text-Independent speaker identification using MFCC and GMM

The area of speaker recognition is concerned with extracting the identity of the person speaking. Speaker recognition can be classified into speaker identification and speaker verification. Speaker identification can be Text-Independent or Text-Dependent. In this paper we lay emphasis on text-Independent speaker identification system where we adopted Mel-Frequency Cepstral Coefficients (MFCC) as the speaker speech feature parameters in the system and the concept of Gaussian Mixture Modeling (GMM) for modeling the extracted speech feature. We used the Maximum Likelihood Ratio Detector algorithm for the decision making process. The experimental study has been performed for various speech time duration and several languages and was conducted around MATLAB 7 language environment. Gaussian mixture speaker model attains high recognition rate for various speech durations.