Effective Utterance Classification with Unsupervised Phonotactic Models 
 This paper describes a method for utterance classification that does not require manual transcription of training data . The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription . In our method, unsupervised training is first used to train a phone n-gram model for a particular domain ; the output of recognition with this model is then passed to a phone-string classifier . The classification accuracy of the method is evaluated on three different spoken language system domains . 