Speaker recognition experiments using connectionist transformation network features

The use of adaptation transforms common in speech recognition systems as features for speaker recognition is an appealing alternative approach to conventional short-term cepstral modelling of speaker characteristics. Recently, we have shown that it is possible to use transformation weights derived from adaptation techniques applied to the Multi Layer Perceptrons that form a connectionist speech recognizer. The proposed method – named Transformation Network features with SVM modelling (TN-SVM) – showed promising results on a sub-set of NIST SRE 2008 and allowed further improvements when it was combined with baseline systems. In this paper, we summarize the recently proposed TN-SVM approach and present new results. First, we explore two alternative approaches that may be used in the absence of high quality speech transcriptions. Second, we present results of the proposed approach with Nuisance Attribute Projection for session variability compensation.