Clustering behaviors of Spoken Dialogue Systems users

Spoken Dialogue Systems (SDS) are natural language interfaces for human-computer interaction. User adaptive dialogue management strategies are essential to sustain the naturalness of interaction. In recent years data-driven methods for dialogue optimization have evolved to be a state of art approach. However these methods need vast amounts of corpora for dialogue optimization. In order to cope with the data requirement of these methods, but also to evaluate the dialogue strategies, user simulations are built. Dialogue corpora used to build user simulation are often not annotated in user´s perspective and thus can only simulate some generic user behavior, perhaps not representative of any user. This paper aims at clustering dialogue corpora into various groups based on user behaviors observed in the form of full dialogues.