Speaker Recognition: Feature Extraction using MFCC and Classification using GMM

Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/ developed by the researchers for feature extraction and for feature matching. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent/independent speaker identification system. The individual Gaussian component of Gaussian Mixture Model (GMM) represents vocal tract configurations that are effective for speaker identification. Gaussian Mixture Modeling(GMM) algorithms are used for generating template and feature matching purpose.