Emotionally Specific Backchanneling in Social Human-Robot Interaction and Human-Human Interaction
Abstract:
Backchanneling models, designed to enhance the interactive capabilities of robots, have primarily been trained on human-human interaction data. However, applying such data directly to social robots raises concerns due to dissimilarities in the way humans and robots exhibit verbal and nonverbal behaviors, particularly in the domain of emotional backchannels. This research aims to address this gap by conducting an exploratory study on the differences in human backchanneling behaviors during interactions with humans and social robots in various emotional contexts (e.g., happy and sad). Our findings reveal significant variations in emotionally specific backchannels between human-human and human-robot interactions under different emotional contexts. These results highlight the importance of designing backchanneling models that are tailored for human-robot interactions.
Jan 1, 2023
