Telling Stories to Robots: The Effect of Backchanneling on a Child's Storytelling
Name
Telling_Stories_to_Robots_The_Effect_of_Backchanne.pdf
Description
Accepted version
Size
2.01 MB
Format
Adobe PDF
Checksum (MD5)
4980e72bf00dfe427dfc91c0d4a43e48
Author(s)
Park, Hae Won
Gelsomini, Mirko
Lee, Jin Joo
Breazeal, Cynthia Lynn
Date Issued
March 2017
Journal
HRI'17: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
Publisher
Association for Computing Machinery (ACM)
Citation
Park, Hae Won, Mirko Gelsomini, Jin Joo Lee, and Cynthia Breazeal. "Telling Stories to Robots: The Effect of Backchanneling on a Child's Storytelling." in HRI'17: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, March 06-09, 2017, Vienna, Austria. © 2017 ACM.
Version
Author's final manuscript
Abstract
While there has been a growing body of work in child-robot interaction, we still have very little knowledge regarding young children's speaking and listening dynamics and how a robot companion should decode these behaviors and encode its own in a way children can understand. In developing a backchannel prediction model based on observed nonverbal behaviors of 4-6 year-old children, we investigate the effects of an attentive listening robot on a child's storytelling. We provide an extensive analysis of young children's nonverbal behavior with respect to how they encode and decode listener responses and speaker cues. Through a collected video corpus of peer-to-peer storytelling interactions, we identify attention-related listener behaviors as well as speaker cues that prompt opportunities for listener backchannels. Based on our findings, we developed a backchannel opportunity prediction (BOP) model that detects four main speaker cue events based on prosodic features in a child's speech. This rule-based model is capable of accurately predicting backchanneling opportunities in our corpora. We further evaluate this model in a human-subjects experiment where children told stories to an audience of two robots, each with a different backchanneling strategy. We find that our BOP model produces contingent backchannel responses that conveys an increased perception of an attentive listener, and children prefer telling stories to the BOP model robot.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Personal Robots Group
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2909824.3020245
