EmoShiftNet: a shift-aware multi-task learning framework with fusion strategies for emotion recognition in multi-party conversations
These findings highlight the importance of modeling emotional transitions in ERC. By leveraging multi-task learning with explicit shift detection, EmoShiftNet enhances contextual awareness and offers more robust performance for multi-party conversational emotion recognition.
Authors
- Hinduja Nirujan
Institutions
- Informatics Institute of Technology (LK)
- Kyoto University of Advanced Science (JP)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-09-03
- DOI
- https://doi.org/10.3389/frai.2025.1618698
- Citations
- 1
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 2.12