Extended multi-word trigger pair language model using data mining technique

Abstract

a good language model is essential to a postprocessing algorithm for recognition systems. Trigger pair model has been used to investigate long distance dependent relationship. However, previous trigger pair model has only one word for its trigger. It is desirable that more words can be observed in the trigger for a better prediction of the triggered word. In this work, we view establishing trigger pair model as mining association rules in a large database and create a multiple words trigger pair model by using an adapted Apriori algorithm. The new trigger pair model can be used in the stage of finding best path from a word lattice as traditional trigger pair model can. Specially, it can be used to correct mistakes remaining in the final result as well. Those mistakes would be unavoidable for other language models.