Feature mapping based on GMM supervector

The channel or inter-session variability problem is one of the most important factors causing recognition errors in speaker recognition systems. In this paper, we have proposed three methods to estimate the channel supervector in the GMM supervector space to deal with this problem, namely EM clustering, PCA and NAP algorithms. Furthermore, feature mapping is applied to the MFCC after the estimation of channel supervector. The EER of the feature mapping system decreases by 34% relatively over the baseline GMM system in the NIST 2006 SRE core test.