Bridging the Gap between Human Emotions and AI: Affective Computing with Multimodal Data
DOI:
https://doi.org/10.15662/IJARCST.2021.0406011Keywords:
Affective Computing, Emotion Recognition, Multimodal Learning, Human–Computer Interaction, Deep Learning, Facial Expression Analysis, Speech Emotion Detection, Physiological Signal Processing, Emotion-Aware AI, Empathetic SystemsAbstract
The rapid evolution of Artificial Intelligence (AI) has significantly enhanced machines’ ability to perceive, reason, and act intelligently. However, one fundamental limitation persists—the inability of AI systems to fully comprehend and respond to human emotions. This gap has given rise to Affective Computing, a multidisciplinary field that aims to bridge emotional intelligence with computational intelligence. The research paper titled “Bridging the Gap Between Human Emotions and AI: Affective Computing with Multimodal Data” explores how the integration of multimodal data—encompassing facial expressions, voice tone, physiological signals, and textual sentiment—can enable machines to recognize, interpret, and simulate human emotions effectively.
The study first outlines the theoretical foundations of affective computing, emphasizing the role of emotion models such as Ekman’s six basic emotions and the dimensional models of valence, arousal, and dominance. It further discusses how affective states are represented in data and how machine learning techniques, including deep neural networks, convolutional architectures, and transformers, can learn to decode these patterns. The research proposes a multimodal fusion framework that processes heterogeneous emotional cues through parallel neural branches—each specialized in processing visual, auditory, and physiological features—and integrates them using attention-based fusion layers. This hybrid architecture allows for dynamic weighting of each modality based on contextual relevance, leading to more accurate emotion recognition.
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