MultiMediate’22: Backchannel Detection and Agreement Estimation in Group Interactions
Philipp Müller, Michael Dietz, Dominik Schiller, Dominike Thomas, Hali Lindsay, Patrick Gebhard, Elisabeth André, Andreas Bulling
Proceedings of the 30th ACM International Conference on Multimedia, pp. 7109–7114, 2022.
Abstract
Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroup Interaction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.Links
Paper: mueller22_mm.pdf
Paper Access: https://dl.acm.org/doi/abs/10.1145/3503161.3551589
BibTeX
@inproceedings{mueller22_mm,
title = {MultiMediate'22: Backchannel Detection and Agreement Estimation in Group Interactions},
author = {M{\"u}ller, Philipp and Dietz, Michael and Schiller, Dominik and Thomas, Dominike and Lindsay, Hali and Gebhard, Patrick and Andr{\'e}, Elisabeth and Bulling, Andreas},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
pages = {7109--7114},
year = {2022},
doi = {10.1145/3503161.355158},
url = {https://dl.acm.org/doi/abs/10.1145/3503161.3551589},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA}
}