Feature adaptation for robust mobile speech recognition

Abstract

Feature adaptation such as feature space maximum likelihood linear regression (FMLLR) is useful for robust mobile speech recognition. However, as the amount of adaptation data increases, feature adaptation performance becomes saturated quickly due to its limitation of global transformation. To handle this problem, we propose regression tree based FMLLR which can adopt multiple transformations as the amount of adaptation data increases. An experimental result shows that the proposed method reduces the recognition error by 11.8% further for speaker adaptation task and by 13.6% further for noisy environment adaptation task compared to the conventional method.