Our goal is to extract information from a telephone call in order to route the call to one of a number of destinations. We assume that we do not know "a priori" the vocabulary used in the application and so we use phonetic recognition followed by identification of salient phone sequences. In previous work, we showed that using a separate language model during recognition for each route gave improved performance over using a single model. However, this technique decodes each utterance in terms of the salient sequences of each call route, which leads to insertion and substitution errors that degrade performance. In this paper, we introduce the use of mixture language models for speech recognition in the context of call route classification. The benefit of technique can has the efficiency of multiple language models to get accurate recognition on salient phoneme sequences; on the other hand, it can give help in classification, even if the size of some call routes have just 50~60 utterances. It avoids building HMMs for some salient phoneme sequences to decide whether it is correct of occurring in the utterance.