Abstract
When interacting with robots in a situated spoken dialogue setting, human dialogue partners tend to assign anthropomorphic and social characteristics to those robots. In this paper, we explore the age and educational level that human dialogue partners assign to three different robotic systems, including an un-embodied spoken dialogue system. We found that how a robot speaks is as important to human perceptions as the way the robot looks. Using the data from our experiment, we derived prosodic, emotional, and linguistic features from the participants to train and evaluate a classifier that predicts perceived intelligence, age, and education level.- Anthology ID:
- W18-5014
- Volume:
- Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 130–139
- Language:
- URL:
- https://aclanthology.org/W18-5014
- DOI:
- 10.18653/v1/W18-5014
- Cite (ACL):
- Sarah Plane, Ariel Marvasti, Tyler Egan, and Casey Kennington. 2018. Predicting Perceived Age: Both Language Ability and Appearance are Important. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 130–139, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Perceived Age: Both Language Ability and Appearance are Important (Plane et al., SIGDIAL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-5014.pdf