Learning Decision Trees to Determine Tu Systems

This paper presents a method for deciding the timing of turn-taking in spoken dialogue systems. This method uses a decision tree learned from the corpus of dialogues between human users and systems in which desirable turn-taking behaviors are annotated by hand. It utilizes a variety of attributes, such as recognition and understanding results and prosodic information. Unlike most of the existing systems it enables spoken dialogue systems to decide the timing of turn-taking based on not only pauses but also other features, so that users can speak to the system even if they put pauses in the middle of their utterances. The result of a preliminary experiment shows that the learned decision tree outperforms the baseline strategy, which takes turn at every user pauses.