Incremental Dialog Clustering For Speech-To-Speech Translation

Application domains for speech-to-speech translation and dialog systems often contain sub-domains and/or task-types for which different outputs are appropriate for a given input. It would be useful to be able to automatically find such subdomain structure in training corpora, and to classify new interactions with the system into one of these sub-domains. To this end. We present a document-clustering approach to such sub-domain classification, which uses a recently-developed algorithm based on von Mises Fisher distributions. We give preliminary perplexity reduction and MT performance results for a speech-to-speech translation system using this model.