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This paper proposes a new technique to enhance the performance of spoken dialogue systems which presents one novel contribution: the automatic correction of some ASR errors by using language models dependent on dialogue states, in conjunction with grammatical rules. These models are optimally selected by computing similarity scores between patterns obtained from uttered sentences and patterns learnt during training. Experimental results with a spoken dialogue system designed for the fast food domain show that our technique allows enhancing word accuracy, speech understanding and task completion rates of a spoken dialogue system by 8.5%, 16.54% and 44.17% absolute, respectively.