Robust features fusion for text independent speaker verification enhancement in noisy environments

So far, many methods have been proposed for speaker verification which provide good results, but their performances reduce in actual noisy environments. A common approach to partially alleviate this problem is the fusion of several methods. In this paper, four systems based on different speech features, i.e., MFCC, IMFCC, LFCC, and PNCC were combined in score-level to improve verification accuracy under clean and noisy speech conditions. The features pairwise and foursome fusion in a speaker verification system based on speaker modeling through the Gaussian mixture model (GMM) were evaluated. TIMIT and NOISEX92 databases were used to implement as the speech and noise datasets, respectively. The experimental results show that the score-level fusion of different feature vectors enhances the accuracy of speaker verification system and this reduces the equal error rates is in some cases up to 44%.