Perceptual borderline for balancing multi-class spontaneous emotional data

Abstract

Speech is a behavioural biometric signal that can provide important information to understand the human intends as well as their emotional status. The paper is centered on the speech-based identification of the seniors’s emotional status during their interaction with a virtual agent playing the role of a health professional coach. Under real conditions, we can just identify a small set of task-dependent spontaneous emotions. The number of identified samples is largely different for each emotion, which results in an imbalanced dataset problem. This research proposes the dimensional model of emotions as a perceptual representation space alternative to the generally used acoustic one. The main contribution of the paper is the definition of a perceptual borderline for the oversampling of minority emotion classes in this space. This limit, based on arousal and valence criteria, leads to two methods of balancing the data: the Perceptual Borderline oversampling and the Perceptual Borderline SMOTE (Synthetic Minority Oversampling Technique). Both methods are implemented and compared to state-of-the-art approaches of Random oversampling and SMOTE. The experimental evaluation was carried out on three imbalanced datasets of spontaneous emotions acquired in human-machine scenarios in three different cultures: Spain, France and Norway. The emotion recognition results obtained by neural networks classifiers show that the proposed perceptual oversampling methods led to significant improvements when compared with the state-of-the art, for all scenarios and languages.The research presented in this paper is conducted as partof the project EMPATHIC and of the MENHIR MSCAaction that have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No 769872 an No 823907 respective