Improving eigenspace-based MLLR adaptation by kernel PCA
Brian Kan-Wing Mak, Roger Wend-Huu Hsiao · 2004
Eigenspace-based MLLR (EMLLR) adaptation has been shown effective for fast speaker adaptation. It applies the basic idea of eigenvoice adaptation, and derives a small set of eigenmatrices using principal component analysis (PCA). The MLLR adapta-tion transformation of a new speaker is then a linear combina-tion of the eigenmatrices. In this paper, we investigate the use of kernel PCA to find the eigenmatrices in the kernel-induced high dimensional feature space so as to exploit possible nonlinearity in the transformation supervector space. In addition, composite kernel is used to preserve the row information in the transfor-mation supervector which, otherwise, will be lost during the mapping to the kernel-induced feature space. We call our new method kernel eigenspace-based MLLR (KEMLLR) adaptation. On a RM adaptation task, we find that KEMLLR adaptation may reduce the word error rate of a speaker-independent model by 11%, and outperforms MLLR and EMLLR adaptation. 1.