Location of an emotionally neutral region in valence-arousal space: Two-class vs. three-class cross corpora emotion recognition evaluations

摘要

There are two main emotion annotation techniques: multidimensional and categories based. In order to conduct experiments on emotional data annotated with different techniques, two-classes emotion mapping strategies (e.g. high-vs. low-arousal) are commonly used. The ”affective computing” community could not specify a location of emotionally neutral area in multi-dimensional emotional space (e.g. valence-arousal-dominance (VAD)). Nonetheless, in the current research a neutral state is added to the standard two-classes emotion classification task. Within experiments a possible location of a neutral arousal region in valence-arousal space was determined. We employed general and phonetic pattern dependent emotion classification techniques for cross-corpora experiments. Emotional models were trained on the VAM dataset (multi-dimensional annotation) and evaluated them on the EMO-DB dataset (categories based annotation).