Emotion recognition is a crucial aspect of human communication, with applications in fields such as psychology, education, and healthcare. Identifying emotions accurately is challenging, as people use a variety of signals to express and perceive emotions. In this study, we address the problem of multimodal emotion recognition using both audio and video signals, to develop a robust and reliable system that can recognize emotions even when one modality is absent. To achieve this goal, we propose a novel architecture based on well-designed feature extractors for each modality and use model-level fusion based on a TFusion block to combine the information from both sources. To be more efficient in real-world scenarios, we trained our model on a compound dataset consisting of RAVDESS, RML, and eNTERFACE'05. It is then evaluated and compared to the state-of-the-art models. We find that our approach performs close to the modern solutions and can recognize emotions accurately when one of the modalities is missing. Additionally, we have developed a real-time emotion recognition application as a part of this work.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1016/j.procs.2023.10.247
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2023
