SVM Speaker Verification using an Incomplete Cholesky Decomposition Sequence Kernel
Jérôme Louradour, Khalid Daoudi, Francis Bach · 2006
The generalized linear discriminant sequence (GLDS) kernel has been showing to provide very good performance in SVM speaker verification in NIST SRE evaluations. The GLDS kernel is based on an explicit mapping of each sequence to a single vector in a feature space using polynomial expansions. Because of practical limitations, these expansions have to be of degree less or equal to 3. In this paper, we generalize the GLDS kernel to allow not only any polynomial degree but also any expansion (possibly infinite dimensional) that defines a Mercer kernel (such as the RBF kernel). To do so, we use low-rank decompositions of the Gram matrix to express the feature space kernel in terms of input space data only. We present experiments on the Biosecure project data. The results show that our new sequence kernel outperforms the GLDS one as well as the one developed in our recent work