A Non-Linear Speaker Adaptation Technique using Kernel Ridge Regression

We propose a non-linear model space transformation for speaker or environment adaptation based on weighted kernel ridge regression (KRR). The transformation is given by a generalized least squares linear regression in a kernel-induced feature space operating on Gaussian mixture model means and having as targets the adaptation frames. Using the "kernel trick", the solution to the optimization problem is obtained by solving a system of linear equations involving the Gram matrix of the input variables. We show that MLLR is a special case of KRR when a linear kernel is employed. Furthermore, we study an efficient low-rank approximation to the kernel matrix termed "rectangle method", where the regressors are chosen to be a small set of clustered adaptation frames. Experiments conducted on the EARS database (English conversational telephone speech) indicate that KRR with a Gaussian RBF kernel outperforms standard regression class-based MLLR