Kernel Eigenspace-based MLLR Adaptation Using Multiple Regression Classes
Roger Wend-Huu Hsiao, Brian Kan-Wing Mak · 2006
Recently, we have been investigating the application of kernel methods to improve the performance of eigenvoice-based adaptation methods by exploiting possible nonlinearity in their original working space. We proposed the kernel eigenvoice adaptation (KEV), and the kernel eigenspace-based MLLR adaptation (KEMLLR). In KEMLLR, speaker-dependent MLLR transformation matrices are mapped to a kernel-induced high dimensional feature space, and kernel principal component analysis (KPCA) is used to derive a set of eigenmatrices in the feature space. A new speaker is then represented by a linear combination of the leading eigenmatrices. In this paper, we further improve KEMLLR by the use of multiple regression classes and the quasi-Newton BFGS optimization algorithm.