Robustness of several kernel-based fast adaptation methods on noisy LVCSR
Brian Kan-Wing Mak, Roger Wend-Huu Hsiao · 2007
We have been investigating the use of kernel methods to im-prove conventional linear adaptation algorithms for fast adap-tation, when there are less than 10s of adaptation speech. On clean speech, we had shown that our new kernel-based adap-tation methods, namely, embedded kernel eigenvoice (eKEV) and kernel eigenspace-based MLLR (KEMLLR) outperformed their linear counterparts. In this paper, we study their unsu-pervised 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. Fur-thermore, KEMLLR adaptation gives the greatest overall im-provement over the speaker-independent model by about 19%. Index Terms: fast adaptation, kernel method, kernel eigenspace-based MLLR, embedded kernel eigenvoice, MAP,