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 interspeaker variability is used in the recognition while intra-speaker variability is ignored. In this paper, we present the VCSVM theory and promising results on nuisance compensation.
Bibliographic reference. Karam, Zahi N. / Campbell, W. M. (2009): "Variability compensated support vector machines applied to speaker verification", In INTERSPEECH-2009, 1555-1558.