LAUSR

Speaker recognition is the process of identifying the proper speaker by analyzing the spectral shape of the speech signal. This process is done by extracting the desired features and matching the features of the speech signal. In this paper, we adopted the Mel frequency cepstrum coefficient (MFCC) technique for extracting the features from the speaker speech sample. These cepstrum coefficients are named as extracted features. The extracted MFCC features are given as input to the modified vector quantization via Linde–Buzo–Gray (modified VQ-LBG) process and expectation maximization (EM) algorithm. Vector quantization technique is mainly used for feature matching where a separate codebook will be generated for each speaker. The EM algorithm is utilized to develop the Gaussian mixture model–universal background model (GMM–UBM). In GMM–UBM model, k means cluster is summed up to consolidate data about the covariance structure of the information and the focuses of the inert Gaussians. From our analysis, the modified VQ-LBG algorithm gives better performance compared to the GMM–UBM model.