The use of Support Vector Machines (SVMs) for speaker verification has become increasingly popular. To handle the dynamic nature of the speech utterances, many SVM-based systems use dynamic kernels. Many of these kernels can be placed into two classes, parametric kernels, where the feature-space consists of parameters from the utterance-dependent model, and derivative kernels, where the derivatives of the utterance log-likelihood with respect to parameters of a generative model are used. This paper contrasts the attributes of these two forms of kernel. Furthermore, the conditions under which the two forms of kernel are identical are described. Two forms of dynamic kernel are examined in detail, based on MLLR-adaptation and mean MAP-adapted models. The performance of these kernels is evaluated on the NIST SRE 2002 dataset. Combining the two forms of kernel together gave a 35% relative reduction in Equal Error Rate compared to the best individual kernel.