Language model adaptation based on the classification of a trigram´s language style feature

In this paper, an adaptation method of the language style of a language model is proposed based on the differences between spoken and written language. Several interpolation methods based on trigram counts are used for adaptation. An interpolation method considering Katz smoothing computes weights according to the confidence score of a trigram. An adaptation method based on the classification of a trigram´s style feature computes weights dynamically according to the trigram´s language style tendency, and several weight generation functions are proposed. Experiments for spoken language on the Chinese corpora show that these methods, especially the method considering both a trigram´s confidence and style tendency, can achieve a reduction in the Chinese character error rate for pinyin-to-character conversion.