Year: 2025
Type: article
Abstract: Enhancing user emotional experience is crucial for improving product appeal and user satisfaction. However, current systems often struggle to integrate explicit and implicit interactions, particularly in capturing subtle emotional signals and dynamically adjusting feedback based on user behavior. To address these challenges, XFormerLite, a lightweight transformer-based model, is proposed to optimize emotional experiences via explicit–implicit signal integration, temporal modeling, and adaptive feedback. By combining explicit signals (e.g., voice and gestures) with implicit signals (e.g., facial expressions and posture), XFormerLite improves emotional engagement and personalizes real-time feedback. Experimental results indicate that XFormerLite achieves an emotion recognition accuracy of 86.2% on the Emo-DB dataset and 84.5% on IEMOCAP, along with significant improvements in response speed and emotion onset delay (EOD). This model presents a promising solution for optimizing emotional experiences in human–product interactions.
DOI: 10.4018/joeuc.387832
Authors: Xin Liu, Wei Bi, Pingchuan Ke, Yan Wang, Liuyi Chen
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4413975968