Toward Mathematical Representation of Emotion: A Deep Multitask Learning Method Based On Multimodal Recognition

To emulate human emotions in agents, the mathematical representation of emotion (an emotional space) is essential for each component, such as emotion recognition, generation, and expression. In this study, we aim to acquire a modality-independent emotional space by extracting shared emotional information from different modalities. We propose a method of acquiring an emotional space by integrating multimodalities on a DNN and combining the emotion recognition task and the unification task. The emotion recognition task learns the representation of emotions, and the unification task learns an identical emotional space from each modality. Through the experiments with audio-visual data, we confirmed that there are differences in emotional spaces acquired from unimodality, and the proposed method can acquire a joint emotional space. We also indicated that the proposed method could adequately represent emotions in a low-dimensional emotional space, such as in five or six dimensions, under this paper's experimental conditions.