Turn-Taking Prediction Based on Detection of Transition Relevance Place

We address turn-taking prediction in which spoken dialogue systems predict when to take the conversational floor. In natural conversations, many turn-taking decisions are arbitrary and subjective. In this study, we propose taking into account the concept of the transition relevance place (TRP) for turn-taking prediction. TRP is defined as a timing when the current speaking turn can be completed and other participants are able to take the turn. We conducted annotation of TRP on a human-robot dialogue corpus, ensuring the objectivity of this annotation among annotators. The proposed turn-taking prediction model adopts a two-step approach that detects TRP at first and then predicts a turn-taking event if TRP is detected. Experimental evaluations demonstrate that the proposed model improves the accuracy of turn-taking prediction by incorporating TRP detection.