Maximum Penalized Likelihood Kernel Regression for Fast Adaptation
Brian Kan-Wing Mak, Tsz-Chung Lai, Ivor Wai-Hung Tsang, James Tin-Yau Kwok · IEEE Transactions on Audio Speech and Language Processing · 2009
This paper proposes a nonlinear generalization of the popularmaximum-likelihoodlinearregression(MLLR) adaptation algorithm using kernel methods. The proposed method, calledmaximumpenalizedlikelihoodkernelregressionadaptation (MPLKR), applies kernel regression with appropriate regularization to determine the affine model transform in a kernel-induced high-dimensional feature space. Although this is not the first attempt of applying kernel methods to conventional linear adaptation algorithms, unlike most of other kernelized adaptation methods such as kernel eigenvoice or kernel eigen-MLLR, MPLKR has the advantage that it is a convex optimization and its solution is always guaranteed to be globally optimal. In fact, the adapted Gaussian means can be obtained analytically by simply solving a system of linear equations. From the Bayesian perspective, MPLKR can also be considered as the kernel version ofmaximumaposteriorilinearregression(MAPLR) adaptation. Supervised and unsupervised speaker adaptation using MPLKR were evaluated on the Resource Management and Wall Street Journal 5K tasks, respectively, achieving a word error rate reduction of 23.6% and 15.5% respectively over the speaker-independently model.