An online incremental language model adaptation method

In this paper, an online incremental language model adaptation method is proposed, which is different from the traditional offline language model adaptation method. There are some problems in the online incremental adaptation. The first one is how to adjust the model parameters online and modify the model incrementally. The second one is how to induce new words and assign initial probabilities to the ngrams related to them. In our application for Chinese character input method editor, the language model is divided into two parts, corresponding to the background (generalpurpose) model and the user model, respectively. A modified maximum a posterior method is proposed for adapting the user model dynamically. Experiments are done to test the proposed method on a Chinese sentence input system and the results show that a satisfying word error rate reduction is obtained when the input articles are of similar topics.