Text-Independent Speaker Identification Using Hidden Markov Model

This paper presents a text-independent speaker identification system based on Mel-Frequency Cepstrum Coefficient (MFCC) feature vectors and Hidden Markov Model (HMM) classifier. The implementation of the HMM is divided into two steps: feature extraction and recognition. In the feature extraction step, the paper reviews MFCCs by which the spectral features of speech signal can be estimated and shows how these features can be computed. In the recognition step, the theory and implementation of HMM are reviewed and followed by an explanation of how HMM can be trained to generate the model parameters using Forward-Backward algorithm and tested using forward algorithm. The HMM is evaluated using data of 40 speakers extracted from Switchboard corpus. Experimental results show an identification rate of about 84%.