Multimodal AI for Emotion Recognition: Integrating Speech, Text, and Facial Expressions
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Emotion recognition has become a pivotal area of research in human-computer interaction, artificial intelligence, and affective computing. While unimodal approaches have shown promise, they are often limited by the inherent ambiguity and subtlety of human emotional expression. This chapter explores the paradigm of Multimodal Artificial Intelligence (AI) for emotion recognition, a more robust approach that integrates information from multiple sources—specifically speech, text, and facial expressions. We delve into the foundational concepts of multimodal systems, from data preprocessing and feature extraction to advanced fusion techniques. A comprehensive literature review is presented, highlighting seminal works and state-of-the-art models that have shaped the field. We then propose a novel hybrid deep learning framework that leverages Convolutional Neural Networks (CNNs) for spatial feature extraction from facial and speech data, and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. The chapter details the proposed methodology, including the architecture, feature extraction pipelines for each modality, and a hybrid fusion strategy designed to maximize inter-modal correlations. An extensive Results and Discussions section presents simulated experimental results on benchmark datasets, demonstrating the superiority of the multimodal approach over unimodal systems. We analyze performance metrics, including accuracy, F1-score, and confusion matrices, and compare different fusion strategies. The chapter concludes with a summary of key findings, a discussion of the challenges and limitations of current methods, and an outlook on future research directions in multimodal emotion recognition, paving the way for more empathetic and intelligent applications.
References
- Rosalind W Picard. Affective computing. MIT press, 2000.
- Jeffrey F Cohn, Zara Ambadar, and Paul Ekman. “Observer-based measurement of facial expression with the Facial Action Coding System”. In: The handbook of emotion elicitation and assessment 1.3 (2007), pp. 203–221.
- Zengzhao Chen et al. “MTLSER: Multi-task learning enhanced speech emotion recognition with pre-trained acoustic model”. In: Expert Systems with Applications 273 (2025), p. 126855.
- Beibut Amirgaliyev et al. “A review of machine learning and deep learning methods for person detection, tracking and identification, and face recognition with applications”. In: Sensors 25.5 (2025), p. 1410.
- Hamza Roubhi et al. “A Novel Approach to Enhancing Performance in 1D-CNN-Based Speech Emotion Recognition Using Mutual Information-Based Feature Selection.” In: Journal of Engineering Science & Technology Review 18.4 (2025).
- Qasim Umer. “Bidirectional encoder representations from transformers (BERT) driven approach for identifying feasible software enhancements”. In: PeerJ Computer Science 11 (2025), e3290.
- You Wu, Qingwei Mi, and Tianhan Gao. “A comprehensive review of multimodal emotion recognition: Techniques, challenges, and future directions”. In: Biomimetics 10.7 (2025), p. 418.
- Ziqi Liu et al. “A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm”. In: Mathematics 13.8 (2025), p. 1218.
- Chung Soo Ahn. “Speech emotion recognition using multimodal data”. PhD thesis. Nanyang Technological University, 2025.
- Mithilaj JS, SA Shanavas, and D Muhammad Noorul Mubarak. “A Review of the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS).” In: Language in India 25.7 (2025).
- Sebastian Ocklenburg et al. “Three-Dimensional Movement Analysis of Hugging in Romantic Couples and Platonic Friends Using Markerless Motion Capture”. In: Journal of Nonverbal Behavior (2025), pp. 1–23.
