A straightforward and efficient implementation of the factor analysis model for speaker verification

Abstract

For a few years, the problem of session variability in textindependent automatic speaker verification is being tackled actively. A new paradigm based on a factor analysis model have successfully been applied for this task. While very efficient, its implementation is demanding. In this paper, the algorithms involved in the eigenchannel MAP model are written down for a straightforward implementation, without referring to previous work or complex mathematics. In addition, a different compensation scheme is proposed where the standard GMM likelihood can be used without any modification to obtain good performance (even without the need of score normalization). The use of the compensated supervectors within a SVM classifier through a distance based kernel is also investigated. Experiments results shows an overall 50% relative gain over the standard GMM-UBM system on NIST SRE 2005 and 2006 protocols (both at the DCFmin and EER). Index Terms: Speaker Verification, Session variability, Factor Analysis, GMM Supervectors.