Outlier removal and fusion techniques for robust speaker recognition applications

Outliers in real time speaker recognition can be viewed as a disturbing element and one of the reason of the degradation of the recognition accuracy. In speaker space, outliers may consider as non-intrinsic speaker's information in clean environment or noise information in noisy environment. So detection of outliers purify the speaker space with most speaker specific feature vectors in both clean and noisy environment. There are several methodology to detect outliers but in this paper we use a distance based method to mitigate the effects of outliers and incorporate fusion techniques to improve the recognition accuracy of speaker recognition system. Distances are taken from Minkowski family up to third order and also Mahalanobis distance which is a probabilistic distance. In fusion methodology we use GMM as a single classifier with complementary feature sets, MFCC and IMFCC. In this paper, we fuse the score of MFCC and IMFCC with a equal weight method. This method not only improves the recognition accuracy but simultaneously improve the detection rate of outliers with respect to the base line feature set.