Machine transliteration is an automatic method for converting words in one language into phonetically equivalent ones in another language. There has been growing interest in the use of machine transliteration to assist machine translation and information retrieval. Three types of machine transliteration models - -grapheme-based, phoneme-based, and hybrid - -have been proposed. Surprisingly, there have been few reports of efforts to utilize the correspondence between source graphemes and source phonemes, although this correspondence plays an important role in machine transliteration. Furthermore, little work has been reported on ways to dynamically handle source graphemes and phonemes. In this paper, we propose a transliteration model that dynamically uses both graphemes and phonemes, particularly the correspondence between them. With this model, we have achieved better performance - -improvements of about 15 to 41% in English-to-Korean transliteration and about 16 to 44% in English-to-Japanese transliteration - -than has been reported for other models. ？ 2006 ACM.