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A Distributed Architecture for Real-time Dialogue and On-task Learning of Efficient Co-operative Turn-taking

Building automatic dialogue systems that match human flexibility and reactivity has proven difficult. Many factors impede the progress of such systems, useful as they may be, from the low-level of real-time audio signal analysis and noise filtering to medium-level turn-taking cues and control signals, all the way up to high-level dialogue intent and content-related interpretation. Of these, we have focussed on the dynamics of turn-taking—the real-time1 control of who has the turn and how turns are exchanged and how to integrate these in an expandable architecture for dialogue generation and control. Manual categorization of silences, prosody and other candidate turn-giving signals, or analysis of corpora to produce static decision trees for this purpose cannot address the high withinand between-individual variability observed in natural interaction. As an alternative, we have