Kanji-to-hiragana conversion based on a language model

In speech recognition systems, a common problem is transcription of new additions to the recognition lexicon into their phonetic symbols. Specific to the Japanese language, such a problem can be dealt with in two steps. We focus on the first step, in which the new lexical entry is converted into a set of hiragana syllabaries, which is almost a phonetic transcription. We propose a conversion scheme which yields the most likely hiragana syllabaries, based on a language model. Results from our evaluations on three test sets are also reported. Although the study is conducted on Japanese only, our approach has applications to Chinese