Multifold Fusion Attention Variant for Emotion Recognition

Emotion recognition is increasingly critical in applications like artificial intelligence, healthcare, and human-computer interaction, enabling systems to understand and respond to human emotions. Existing methods often rely on pre-extracted features and simplistic fusion strategies, limiting their effectiveness in handling real-world, heterogeneous multimodal data such as audio, video, and Electroencephalography. This paper introduces the multifold fusion attention variant, an end-to-end framework that processes raw data without feature preprocessing, addressing the challenges of existing approaches. Multifold fusion attention variant employs a novel attention-based intermediate fusion mechanism and dynamic modality weighting, enabling it to adapt to varying data quality and capture both short- and long-range dependencies. Tested on the EAV dataset, the multifold fusion attention variant outperforms existing models, achieving an accuracy of 91.2% with a 7.39% improvement over baselines like TransNet. The results demonstrate multifold fusion attention variant robustness and its potential to advance emotion recognition in real-world applications.