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Several CALL systems have two acoustic models to evaluate a learner's pronunciation. In order to achieve high performance for evaluation, speaker adaptation method is introduced in CALL system. It requires adaptation data of a target language, however, a learner cannot pronounce correctly. In this paper, we proposed two types of new speaker adaptation methods for CALL system. The new methods only require learner's utterance of the native language for adaptation. The first method is an algorithm to adapt acoustic models using bilingual's utterances. The speaker-independent acoustic models of native and target languages are adapted to a bilingual speaker once, then they are adapted to the learner again using the learner's speech of the native language. Phoneme recognition accuracy is about 5% higher than the baseline method. The second method is a training algorithm of an acoustic model. It can robustly train bilinguals' model from a few bilinguals' utterances. Phoneme recognition accuracy is about 10% higher than the baseline method.