Robustness of several kernel-based fast adaptation methods on noisy LVCSR

We have been investigating the use of kernel methods to improve conventional linear adaptation algorithms for fast adaptation, when there are less than 10s of adaptation speech. On clean speech, we had shown that our new kernel-based adaptation methods, namely, embedded kernel eigenvoice (eKEV) and kernel eigenspace-based MLLR (KEMLLR) outperformed their linear counterparts. In this paper, we study their unsupervised adaptation performance under additive and convoluted noises using the Aurora4 Corpus, with no assumption or prior knowledge of the noise type and its level. It is found that both eKEV and KEMLLR adaptation continue to outperform MAP and MLLR, and the simple reference speaker weighting (RSW) algorithm continues to perform favorably with KEMLLR. Furthermore, KEMLLR adaptation gives the greatest overall improvement over the speaker-independent model by about 19%.