Telling Stories to Robots The Effect of Backchanneling on a Child's Storytelling

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 bac...

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Bibliographic Details
Published in:2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI pp. 100 - 108
Main Authors: Park, Hae Won, Gelsomini, Mirko, Lee, Jin Joo, Breazeal, Cynthia
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 06.03.2017
Series:ACM Conferences
Subjects:
Computational modeling
Computer systems organization > Dependable and fault-tolerant systems and networks > Redundancy
Computer systems organization > Embedded and cyber-physical systems > Embedded systems
Computer systems organization > Embedded and cyber-physical systems > Robotics
Data collection
Hidden Markov models
Linear regression
Networks > Network properties > Network reliability
Predictive models
Robots
Software
speaker cues
storytelling
backchannel prediction
robot listener
nonverbal communication
listener response
ISBN:9781450343367, 1450343368
ISSN:2167-2148
Online Access:Get full text
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Summary: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.
ISBN:9781450343367
1450343368
ISSN:2167-2148
DOI:10.1145/2909824.3020245