Towards a Real-time Measure of the Perception of Anthropomorphism in Human-robot Interaction

Tsfasman, M.; Saravanan, Avinash; Viner, Dekel; Goslinga, Daan; De Wolf, Sarah; Raman, C.A.; Jonker, C.M.; Oertel, Catharine

doi:10.1145/3475959.3485394

Towards a Real-time Measure of the Perception of Anthropomorphism in Human-robot Interaction

Title

Towards a Real-time Measure of the Perception of Anthropomorphism in Human-robot Interaction

Author

Tsfasman, M. (TU Delft Interactive Intelligence)
Saravanan, Avinash (Student TU Delft)
Viner, Dekel (Student TU Delft)
Goslinga, Daan (Student TU Delft)
De Wolf, Sarah (Student TU Delft)
Raman, C.A. (TU Delft Pattern Recognition and Bioinformatics)
Jonker, C.M. (TU Delft Interactive Intelligence)
Oertel, Catharine (TU Delft Interactive Intelligence)

Date

2021

Abstract

How human-like do conversational robots need to look to enable long-term human-robot conversation? One essential aspect of long-term interaction is a human's ability to adapt to the varying degrees of a conversational partner's engagement and emotions. Prosodically, this can be achieved through (dis)entrainment. While speech-synthesis has been a limiting factor for many years, restrictions in this regard are increasingly mitigated. These advancements now emphasise the importance of studying the effect of robot embodiment on human entrainment. In this study, we conducted a between-subjects online human-robot interaction experiment in an educational use-case scenario where a tutor was either embodied through a human or a robot face. 43 English-speaking participants took part in the study for whom we analysed the degree of acoustic-prosodic entrainment to the human or robot face, respectively. We found that the degree of subjective and objective perception of anthropomorphism positively correlates with acoustic-prosodic entrainment.

Subject

acoustic-prosodic entrainment
human-robot interaction
multi-modal
prosody

To reference this document use:

http://resolver.tudelft.nl/uuid:fa7f021c-3f31-4e63-90a1-5954009f3661

DOI

https://doi.org/10.1145/3475959.3485394

Publisher

Association for Computing Machinery (ACM)

ISBN

978-1-4503-8679-1

Source

MuCAI 2021 - Proceedings of the 2nd ACM Multimedia Workshop on Multimodal Conversational AI, co-located with ACM MM 2021