Abstract
This paper describes data collection and the first explorative research on the AICO Multimodal Corpus. The corpus contains eye-gaze, Kinect, and video recordings of human-robot and human-human interactions, and was collected to study cooperation, engagement and attention of human participants in task-based as well as in chatty type interactive situations. In particular, the goal was to enable comparison between human-human and human-robot interactions, besides studying multimodal behaviour and attention in the different dialogue activities. The robot partner was a humanoid Nao robot, and it was expected that its agent-like behaviour would render humanrobot interactions similar to human-human interaction but also high-light important differences due to the robot’s limited conversational capabilities. The paper reports on the preliminary studies on the corpus, concerning the participants’ eye-gaze and gesturing behaviours,which were chosen as objective measures to study differences in their multimodal behaviour patterns with a human and a robot partner.- Anthology ID:
- 2020.lrec-1.70
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 559–564
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.70
- DOI:
- Cite (ACL):
- Kristiina Jokinen. 2020. The AICO Multimodal Corpus – Data Collection and Preliminary Analyses. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 559–564, Marseille, France. European Language Resources Association.
- Cite (Informal):
- The AICO Multimodal Corpus – Data Collection and Preliminary Analyses (Jokinen, LREC 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.70.pdf