Hybrid Frameworks for Emotion Recognition Using Multimodal Human Signals
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This chapter presents a comprehensive analysis of hybrid frameworks for emotion recognition using multimodal human signals. We explore the fusion of facial expressions, speech, and physiological signals to create robust and accurate emotion recognition systems. The chapter begins with an introduction to the field, followed by a thorough literature review of existing unimodal and multimodal approaches. We then propose a novel hybrid fusion methodology that leverages the strengths of early, late, and attentionbased fusion techniques. The proposed framework is evaluated on the CMU-MOSEI and IEMOCAP datasets, demonstrating superior performance compared to traditional methods. The results and discussion section provides a detailed analysis of the model’s accuracy, precision, recall, and F1-score, along with per-emotion performance and a confusion matrix. We also discuss the computational complexity and real-time performance of the proposed system. The chapter concludes with a summary of our findings and a discussion of future research directions in the field of multimodal emotion recognition.
References
- Gyanendra K Verma and Uma Shanker Tiwary. “Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals”. In: NeuroImage 102 (2014), pp. 162–172.
- Yucel Cimtay, Erhan Ekmekcioglu, and Seyma Caglar-Ozhan. “Cross-subject multimodal emotion recognition based on hybrid fusion”. In: IEEE Access 8 (2020), pp. 168865–168878.
- Carlos Busso et al. “IEMOCAP: Interactive emotional dyadic motion capture database”. In: Language resources and evaluation 42.4 (2008), pp. 335–359.
- Amir Zadeh et al. “Mosi: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos”. In: arXiv preprint arXiv:1606.06259 (2016).
- Zhuohang Li et al. “CH-CEMS: A Chinese Multi-Concept Benchmark Dataset Towards Explainable Multi-Modal Sentiment Analysis”. In: Proceedings of the Internationa Conference on Learning Representations (ICLR). OpenReview preprint. 2026.
- Fakir Mashuque Alamgir and Md Shafiul Alam. “Hybrid multi-modal emotion recognition framework based on InceptionV3DenseNet”. In: Multimedia Tools and Applications 82.26 (2023), pp. 40375–40402.
- Pratima Singh et al. “Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set”. In: Multimedia Tools and Applications 84.1 (2025), pp. 1–36.
- Luntian Mou et al. “Driver emotion recognition with a hybrid attentional multimodal fusion framework”. In: IEEE Transactions on Affective Computing 14.4 (2023), pp. 2970–2981.
- Johannes Wagner, Elisabeth Andr´e, and Frank Jung. “Smart sensor integration: A framework for multimodal emotion recognition in real-time”. In: 2009 3rd international conference on affective computing and intelligent interaction and workshops. IEEE. 2009, pp. 1–8.
Principles of Hybrid Intelligent Systems
