A Novel Sequential Monte Carlo Framework for Predicting Ambiguous Emotion States

When continuous emotion labelling of natural (non-acted) data is desired, it is typically collected from multiple annotators. However, most automatic emotion recognition systems trained on such data ignore disagreement between annotators and only models the average rating, despite the observation that the degree of disagreement would reflect the ambiguity and subtlety in every expression of emotions. In this paper, we propose a novel Sequential Monte Carlo framework that models the perceived emotion as time-varying distributions that allows for ambiguity to be incorporated. Additionally, we present alternative measures that consider both the similarity of prediction to the multiple labels, as well as whether the degree of ambiguity in the prediction and labels. The proposed system was validated on the publicly available RECOLA dataset.