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International audience; Understanding children's conversational skills is crucial for understanding their social, cognitive, and linguistic development, with important applications in health and education. To develop theories based on quantitative studies of conversational development, we need (i) data recorded in naturalistic contexts (e.g., child-caregiver dyads talking in their daily environment) where children are more likely to show much of their conversational competencies, as opposed to controlled laboratory contexts which typically involve talking to a stranger (e.g., the experimenter); (ii) data that allows for clear access to children's multimodal behavior in face-to-face conversations; and (iii) data whose acquisition method is cost-effective with the potential of being deployed at a large scale to capture individual and cultural variability. The current work is a first step to achieving this goal. We built a corpus of video chats involving children in middle childhood (6–12 years old) and their caregivers using a weakly structured word-guessing game to prompt spontaneous conversation. The manual annotations of these recordings have shown a similarity in the frequency distribution of multimodal communicative signals from both children and caregivers. As a case study, we capitalize on this rich behavioral data to study how verbal and non-verbal cues contribute to the children's conversational coordination. In particular, we looked at how children learn to engage in coordinated conversations, not only as speakers but also as listeners, by analyzing children's use of backchannel signaling (e.g., verbal “mh” or head nods) during these conversations. Contrary to results from previous in-lab studies, our use of a more spontaneous conversational setting (as well as more adequate controls) revealed that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. Our work demonstrates the usefulness of recent technology in video calling for acquiring quality data that can be used for research on children's conversational development in the wild.

Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in attention inference, we conduct investigations into real-world interactions of children (5–6 years old) storytelling with their peers. Through indepth analysis of human–human interaction data, we first identify nonverbal speaker cues (i.e., backchannel-inviting cues) and listener responses (i.e., backchannel feedback). We then demonstrate how speaker cues can modify the interpretation of attention-related backchannels as well as serve as a means to regulate the responsiveness of listeners. We discuss the design implications of our findings toward our primary goal of developing attention recognition models for storytelling robots, and we argue that social robots can proactively use speaker cues to form more accurate inferences about the attentive state of their human partners., National Science Foundation (U.S.) (grant IIS-1138986), William Asbjornsen Albert Memorial Fellowship, Dolores Zohrab Liebmann Fellowship