Predicting Backchannel Signaling in Child-Caregiver Multimodal Conversations

Abstract

Conversation requires cooperative social interaction between interlocutors. In particular, active listening through backchannel signaling (hereafter BC) i.e., showing attention through verbal (short responses like "Yeah") and non-verbal behaviors (e.g. smiling or nodding) is crucial to managing the flow of a conversation and it requires sophisticated coordination skills. How does BC develop in childhood? Previous studies were either conducted in highly controlled experimental settings or relied on qualitative corpus analysis, which does not allow for a proper understanding of children's BC development, especially in terms of its collaborative/coordinated use. This paper aims at filling this gap using a machine learning model that learns to predict children's BC production based on the interlocutor's inviting cues in child-caregiver naturalistic conversations. By comparing BC predictability across children and adults, we found that, contrary to what has been suggested in previous in-lab studies, children between the ages of 6 and 12 can actually produce and respond to backchannel inviting cues as consistently as adults do, suggesting an adult-like form of coordination.