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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 learners 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.