Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning
Shiyu Duan, Ziyi Wang, Shixiao Wang, Mengmeng Chen, Runsheng Zhang

TL;DR
This paper presents a multi-modal deep learning system that enhances emotion recognition in user interfaces by integrating facial, speech, and textual cues, leading to more empathetic and natural human-computer interactions.
Contribution
It introduces a novel multi-branch Transformer model for real-time emotion recognition using multi-modal data, improving accuracy over traditional methods.
Findings
Significant improvement in emotion recognition accuracy and F1 scores.
Effective integration of facial, speech, and textual data for emotional understanding.
Enhanced emotional responsiveness in user interfaces.
Abstract
In an era where user interaction with technology is ubiquitous, the importance of user interface (UI) design cannot be overstated. A well-designed UI not only enhances usability but also fosters more natural, intuitive, and emotionally engaging experiences, making technology more accessible and impactful in everyday life. This research addresses this growing need by introducing an advanced emotion recognition system to significantly improve the emotional responsiveness of UI. By integrating facial expressions, speech, and textual data through a multi-branch Transformer model, the system interprets complex emotional cues in real-time, enabling UIs to interact more empathetically and effectively with users. Using the public MELD dataset for validation, our model demonstrates substantial improvements in emotion recognition accuracy and F1 scores, outperforming traditional methods. These…
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Taxonomy
TopicsEmotion and Mood Recognition · Color perception and design
MethodsAttention Is All You Need · Linear Layer · Dense Connections · Label Smoothing · Absolute Position Encodings · Layer Normalization · Position-Wise Feed-Forward Layer · Adam · Multi-Head Attention · Residual Connection
