A new language model adaptation framework using modification of structures of background corpus and language model

This paper presents a new framework of language model adaptation based on modification of structures of background corpus and language model. The widely used adaptation approach such as Linear Interpolation Method (LI) and Minimum Discrimination Information (MDI) method are used as the approaches to modify structure of trained background language model in new framework, while Maximum A Posteriori approach (MAP) is used as the method of modifying structure of background corpus. Experiments show that both techniques in the framework yield a significant reduction in perplexity over LI, MAP and MDI method in general adaptation framework about 5.2%, 12.2% and 36.8% respectively.