Multimodal and Multitask Approach to Listener's Backchannel Prediction

The listener's backchannel has the important function of encouraging a current speaker to hold their turn and continue to speak, which enables smooth conversation. The listener monitors the speaker's turn-management (a.k.a. speaking and listening) willingness and his/her own willingness to display backchannel behavior. Many studies have focused on predicting the appropriate timing of the backchannel so that conversational agents can display backchannel behavior in response to a user who is speaking. To the best of our knowledge, none of them added the prediction of turn-changing and participants' turn-management willingness to the backchannel prediction model in dyad interactions. In this paper, we proposed a novel backchannel prediction model that can jointly predict turn-changing and turn-management willingness. We investigated the impact of modeling turn-changing and willingness to improve backchannel prediction. Our proposed model is based on trimodal inputs, that is, acoustic, linguistic, and visual cues from conversations. Our results suggest that adding turn-management willingness as a prediction task improves the performance of backchannel prediction within the multi-modal multi-task learning approach, while adding turn-changing prediction is not useful for improving the performance of backchannel prediction.