Robust representations for out-of-domain emotions using Emotion Profiles

The proper representation of emotion is of vital importance for human-machine interaction. A correct understanding of emotion would allow interactive technology to appropriately respond and adapt to users. In human-machine interaction scenarios it is likely that over the course of an interaction, the human interaction partner will express an emotion not seen during the training of the machine's emotion models. It is therefore crucial to prepare for such eventualities by developing robust representations of emotion that can distinctly represent emotions regardless of whether the data were seen during training of the representation. This novel work demonstrates that an Emotion Profile (EP) representation introduced in [1], a representation composed of the confidences of four binary emotion-specific classifiers, can distinctly represent emotions unseen during training. The classification accuracy increases by only 0.35% over the full dataset when the data excluded from the EP training is included. The results demonstrate that EPs are a robust method for emotion representation.