Towards Addressing Label Ambiguity in Sequential Emotional Responses Through Distribution Learning

This work highlights the challenge of labeling data with single-label categories, as there may be ambiguity in the assigned labels. This ambiguity arises when a data sample, which can be influenced by previous affective events is labeled with a single-label category (known as priming). Label distribution learning (LDL) is proposed as an approach to contend with the ambiguity among labels. This approach has been relatively unexplored in the field of affective computing. In this work, an experiment is designed to explore the benefits of employing LDL using specifically the SEED and SEED-V datasets. In these datasets, different emotions are induced by exposing participants to a sequence of stimuli (videoclip watching). However, these datasets provide single labels, where each data point corresponds to one affective state or emotion. Due to the lack of label distributions within existing benchmarks, label enhancement serves as a preparatory step, whose goal is to compute label distributions from the feature space and single labels before training a label distribution learning model. Experimental results show that the LDL approach reduces confusion with respect to the emotion induced in the previous trial. Distribution learning is an approach that can help to further improve the prediction of affect, which to date remains a difficult and ambiguous concept to label.