Domain-specific dialogue system is an important and also commercial-practicable application of speech recognition technique, and it is very helpful to decrease the search space in the aspects of accuracy improvement and search time reduction in speech recognition. Adequate use of dialogue-state-dependent language models in dialogue systems can decrease the search space greatly if a reasonable prediction of the dialogue states is feasible, and will make a dialogue system more robust in real practice. This paper presents a novel method of selecting different rule-based sub-language-models based on dialogue states to decrease the search space, which will select an adequate rulebased sub-language model in different conversation step according to the context. Experiments show that it is simple and effective in improving accuracy and recognition speed, and will be very useful in small and medium task domain.