11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Regularized-MLLR Speaker Adaptation for Computer-Assisted Language Learning System

Dean Luo (1), Yu Qiao (1), Nobuaki Minematsu (1), Yutaka Yamauchi (2), Keikichi Hirose (1)

(1) University of Tokyo, Japan
(2) Tokyo International University, Japan

In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assisted Language Learning (CALL) systems. This method uses the 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 pro-posed method outperforms conventional MLLR adaption in pronunciation evaluation and can avoid the problem of over adaptation.

Full Paper

Bibliographic reference.  Luo, Dean / Qiao, Yu / Minematsu, Nobuaki / Yamauchi, Yutaka / Hirose, Keikichi (2010): "Regularized-MLLR speaker adaptation for computer-assisted language learning system", In INTERSPEECH-2010, 594-597.