用cGAN生成情感数据，提升多模态情绪识别准确率
Emotion Detection Using Conditional Generative Adversarial Networks (cGAN): A Deep Learning Approach
- 设计cGAN框架融合文本、音频与表情数据
- 合成情感数据后分类准确率显著提升
- 适合做智能交互系统的情绪感知研究
本文提出一种基于条件生成对抗网络（cGAN）的深度学习情绪检测方法。不同于依赖单一数据类型的传统单模态技术，我们构建了融合文本、音频和面部表情的多模态框架。所提出的cGAN架构通过生成富含情感的合成数据，提升了多模态情绪分类的准确性。实验结果表明，该方法在情绪识别性能上显著优于基线模型。本工作展示了cGAN在增强人机交互系统中实现更细腻情绪理解方面的潜力。
原文摘要 · Abstract (English)
This paper presents a deep learning-based approach to emotion detection using Conditional Generative Adversarial Networks (cGANs). Unlike traditional unimodal techniques that rely on a single data type, we explore a multimodal framework integrating text, audio, and facial expressions. The proposed cGAN architecture is trained to generate synthetic emotion-rich data and improve classification accuracy across multiple modalities. Our experimental results demonstrate significant improvements in emotion recognition performance compared to baseline models. This work highlights the potential of cGANs in enhancing human-computer interaction systems by enabling more nuanced emotional understanding.
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