Weighted I-Vector Based Text-Independent Speaker Verification System

Abstract

Speaker recognition is one of the most common and user-friendly methods for biological signals based people identification. Nowadays, Speaker verification based on factor analysis and i-vector space has a great impact on the performance improvement of these systems. In this paper, a method is proposed for weighting the model and test vectors, which utilizes the statistical characteristics of target training vectors. The effect of the use of weighted vectors on the accuracy of scoring and the performance of the entire speaker verification system was evaluated for Mel-frequency cepstral coefficients (MFCC) and power-normalized cepstral coefficients (PNCC) feature vectors, and two scoring methods, i.e., the cosine distance and probabilistic linear discriminant analysis (PLDA). TIMIT database has been used in the evaluation of the system. The test results indicate that the use of proposed weighted vectors reduces the error rate of the speaker verification system significantly.