@inproceedings {pub4638,
	title = {Addressing data sparsity by combining unsupervised and semi-supervised learning for multimodal user state recognition},
	author = {Hendric Voss AND Heiko Wersing AND Stefan Kopp},
	year = {2021},
	month = {October},
	abstract = {Detecting mental states of human users is crucial for the develop-
ment of cooperative and intelligent robots, as it enables the robot
to understand the user{\textquoteright}s intentions and desires. Despite their im-
portance, it is difficult to obtain a large amount of high quality
data for training automatic recognition algorithms as the time and
effort required to collect and label such data is prohibitively high.
In this paper we present a multimodal machine learning approach
for detecting dis-/agreement and confusion states in a human-robot
interaction environment, using just a small amount of manually
annotated data. We collect a data set by conducting a human-robot
interaction study and develop a novel preprocessing pipeline for
our machine learning approach. By combining unsupervised and
semi-supervised architectures, we are able to accurately predict
different user states using a small amount of labeled data, combined
with a large unlabeled data set.},
	publisher = {ACM},
	booktitle = {ACM International Conference on Multimodal Interaction (ICMI) Workshops}
}