Regularized-MLLR speaker adaptation for computer-assisted language learning system

In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assisted Language Learning (CALL) systems. This method uses a linear combination of a group of teachers’ transformation matrices to represent each target learner’s transformation matrix, thus avoids the over-adaptation problem that erroneous pronunciations come to be judged as good pronunciations after conventional MLLR speaker adaptation, which uses learners’ “imperfect” speech as target utterances of adaptation. Experiments of automatic scoring and error detection on public databases show that the proposed method outperforms conventional MLLR adaption in pronunciation evaluation and can avoid the problem of over adaptation.