Connectionist Transformation Network Features for Speaker Recognition

摘要

Alternative approaches to conventional short-term cepstral modelling of speaker characteristics have been proposed and successfully incorporated to current state-of-the art systems for speaker recognition. Particularly, the use of adaptation transforms employed in speech recognition systems as features for speaker recognition is one of the most appealing recent proposals. In this paper, we also explore the use of adaptation transform based features for speaker recognition. However, we consider transformation weights derived from adaptation techniques applied to the Multi Layer Perceptrons that form a connectionist speech recognizer, instead of using transforms of Gaussian models. Modelling of the high-dimensionality vectors extracted from the transforms is done with support vector machines (SVM). The proposed method —named Transformation Network features with SVM modelling (TN-SVM)- is assessed and compared to GMM-UBM and Gaussian Super vector systems on a sub-set of NIST SRE 2008. The proposed technique shows promising results and permits further improvements when it is combined with baseline systems.