Speech Features Evaluation for Small Set Automatic Speaker Verification Using GMM-UBM System

Abstract

This paper overviews the application sphere of speaker verification systems and illustrates the use of the Gaussian mixture model and the universal background model (GMM-UBM) in an automatic text-independent speaker verification task. The experimental evaluation of the GMM-UBM system using different speech features is conducted on a 50 speaker set and a result is presented. Equal error rate (EER) using 256 component Gaussian mixture model and feature vector containing 14 mel frequency cepstral coefficients (MFCC) and the voicing probability is 0,76 %. Comparing to standard 14 MFCC vector 23,7 % of EER improvement was acquired.