Wilson, E.; Patel, R.; Taylor, A.; Jones, L. Enhanced Multimodal Integration Using TriFusion Networks for Comprehensive Emotion Analysis. Preprints2024, 2024080576. https://doi.org/10.20944/preprints202408.0576.v1
APA Style
Wilson, E., Patel, R., Taylor, A., & Jones, L. (2024). Enhanced Multimodal Integration Using TriFusion Networks for Comprehensive Emotion Analysis. Preprints. https://doi.org/10.20944/preprints202408.0576.v1
Chicago/Turabian Style
Wilson, E., Ava Taylor and Liam Jones. 2024 "Enhanced Multimodal Integration Using TriFusion Networks for Comprehensive Emotion Analysis" Preprints. https://doi.org/10.20944/preprints202408.0576.v1
Abstract
In this work, we introduce the TriFusion Network, an innovative deep learning framework designed for the simultaneous analysis of auditory, visual, and textual data to accurately assess emotional states. The architecture of the TriFusion Network is uniquely structured, featuring both independent processing pathways for each modality and integrated layers that harness the combined strengths of these modalities to enhance emotion recognition capabilities. Our approach addresses the complexities inherent in multimodal data integration, with a focus on optimizing the interplay between modality-specific features and their joint representation. Extensive experimental evaluations on the challenging AVEC Sentiment Analysis in the Wild dataset highlight the TriFusion Network's robust performance. It significantly outperforms traditional models that rely on simple feature-level concatenation or complex score-level fusion techniques. Notably, the TriFusion Network achieves Concordance Correlation Coefficients (CCC) of 0.606, 0.534, and 0.170 for the arousal, valence, and liking dimensions respectively, demonstrating substantial improvements over existing methods. These results not only confirm the effectiveness of the TriFusion Network in capturing and interpreting complex emotional cues but also underscore its potential as a versatile tool in real-world applications where accurate emotion recognition is critical.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
