Variability Compensated Support Vector Machines Applied to Speaker Verification.

摘要

Speaker verification using SVMs has proven successful, specifically using the GSV Kernel with nuisance attribute projection (NAP). Also, the recent popularity and success of joint factor analysis has led to promising attempts to use speaker factors directly as SVM features. NAP projection and the use of speaker factors with SVMs are methods of handling variability in SVM speaker verification: NAP by removing undesirable nuisance variability, and using the speaker factors by forcing the discrimination to be performed based on inter-speaker variability. These successes have led us to propose a new method we call variability compensated SVM (VCSVM) to handle both inter and intra-speaker variability directly in the SVM optimization. This is done by adding a regularized penalty to the optimization that biases the normal to the hyperplane to be orthogonal to the nuisance subspace or alternatively to the complement of the subspace containing the inter-speaker variability. This bias will attempt to ensure that inter-speaker variability is used in the recognition while intra-speaker variability is ignored. In this paper we present the theory and promising results on nuisance compensation.