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.

We would like to revisit a simple fast adaptation technique called reference speaker weighting (RSW). RSW is similar to eigenvoice (EV) adaptation, and simply requires the model of a new speaker to lie on the span of a set of reference speaker vectors. In the original RSW, the reference speakers are computed through a hierarchical speaker clustering (HSC) algorithm using information such as the gender and speaking rate. We show in this paper that RSW adaptation may be improved if those training speakers that have the highest likelihoods of the adaptation data are selected as the reference speakers; we call them the maximum-likelihood (ML) reference spers.ak- When RSW adaptation was evaluated on WSJ0 using 5s of adaptation speech, the word error rate reduction can be boosted from 2.54% to 9.15% by using 10 ML reference speakers instead of reference speakers determined from HSC. Moreover, when compared with EV, MAP, MLLR, and eKEV on fast adaptation, we are surprised that the algorithmically simplest RSW technique actually gives the best performance.