Context-based Multimodal Machine Learning on Game Oriented Data for Affective State Recognition

Sammanfattning: Affective computing is an essential part of Human-Robot Interaction, where knowing the human’s emotional state is crucial to create an interactive and adaptive social robot. Previous work has mainly been focusing on using unimodal or multimodal sequential models for Affective State Recognition. However, few have included context-based information with their models to boost performance. In this paper, context-based features are tested on a multimodal Gated Recurrent Unit model with late fusion on game oriented data. It shows that using context-based features such as game state can significantly increase the performance of sequential multimodal models on game oriented data.