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Detecting mental states of human users is crucial for the development ofcooperative and intelligent robots, as it enables the robot to understand theuser's intentions and desires. Despite their importance, it is difficult toobtain a large amount of high quality data for training automatic recognitionalgorithms as the time and effort required to collect and label such data isprohibitively high. In this paper we present a multimodal machine learningapproach for detecting dis-/agreement and confusion states in a human-robotinteraction environment, using just a small amount of manually annotated data.We collect a data set by conducting a human-robot interaction study and developa novel preprocessing pipeline for our machine learning approach. By combiningsemi-supervised and supervised architectures, we are able to achieve an averageF1-score of 81.1\% for dis-/agreement detection with a small amount of labeleddata and a large unlabeled data set, while simultaneously increasing therobustness of the model compared to the supervised approach.

Addressing Data Scarcity in Multimodal User State Recognition by Combining Semi-Supervised and Supervised Learning