Joint Deep Cross-Domain Transfer Learning for Emotion Recognition
This work addresses generalization issues in emotion recognition for applications like human-computer interaction, though it appears incremental as it builds on existing transfer learning methods.
The paper tackled the problem of insufficient training data and poor generalization in emotion recognition by proposing a joint deep cross-domain transfer learning strategy, which surpassed state-of-the-art transfer learning schemes on three benchmark datasets.
Deep learning has been applied to achieve significant progress in emotion recognition. Despite such substantial progress, existing approaches are still hindered by insufficient training data, and the resulting models do not generalize well under mismatched conditions. To address this challenge, we propose a learning strategy which jointly transfers the knowledge learned from rich datasets to source-poor datasets. Our method is also able to learn cross-domain features which lead to improved recognition performance. To demonstrate the robustness of our proposed framework, we conducted experiments on three benchmark emotion datasets including eNTERFACE, SAVEE, and EMODB. Experimental results show that the proposed method surpassed state-of-the-art transfer learning schemes by a significant margin.