Text-Independent Speaker Identification Using GMM With Universal Background Model

State-of-the-art of speaker recognition is fully advanced nowadays. There are various well-known technologies used to process voice, including Gaussian mixture models. The paper presents our work on speaker identification from his voice. In our experiment we first extract key features from a speech signal using VOICEBOX [1]toolbox in MATLAB. These features are represented by a matrix of mel frequency cepstral coefficients (MFCC). Then, applying MSR Identity Toolbox, we build an identity for each person enrolled in our system using statistical Gaussian Mixture Model Universal Background Model (GMM-UBM) and features extracted from speech signals. Universal Background Model improves Gaussian Mixture Model statistical computation for decision logic in speaker verification task. As a corpus, we used TIMIT database for our experiments. Finally, we compared the recognition accuracy for several different scenarios of our experiments.