This paper describes the derivation of a sequence kernel that transforms speech utterances into probabilistic vectors for classification in an expanded feature space. The sequence kernel is built upon a set of Gaussian basis functions, where half of the basis functions contain speaker specific information while the other half implicates the common characteristics of the competing background speakers. The idea is similar to that in the Gaussian mixture model – universal background model (GMM-UBM) system, except that the Gaussian densities are treated individually in our proposed sequence kernel, as opposed to two mixtures of Gaussian densities in the GMM-UBM system. The motivation is to exploit the individual Gaussian components for better speaker discrimination. Experiments on NIST 2001 SRE corpus show convincing results for the probabilistic sequence kernel approach.