Regularized-MLLR speaker adaptation for computer-assisted language learning system
Dean Luo, Yu Qiao, Nobuaki Minematsu, Yutaka Yamauchi, Keikichi Hirose · 2010
In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assisted Language Learn-ing (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 scor-ing and error detection on public databases show that the pro-posed method outperforms conventional MLLR adaption in pronunciation evaluation and can avoid the problem of over adaptation. Index Terms: Computer Assisted Language Learning (CALL), speaker adaption, pronunciation evaluation, goodness of pro-nunciation (GOP), maximum likelihood linear regression (MLLR) 1.