Paper: Effective Utterance Classification With Unsupervised Phonotactic Models

ACL ID N03-1001
Title Effective Utterance Classification With Unsupervised Phonotactic Models
Venue Human Language Technologies
Session Main Conference
Year 2003
Authors

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 conven- tional word-trigram recognition requiring man- ual 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 ac- curacy of the method is evaluated on three dif- ferent spoken language system domains.