Emotion Detection Using Conditional Generative Adversarial Networks (cGAN): A Deep Learning Approach
This work addresses emotion recognition for human-computer interaction systems, but it appears incremental as it applies an existing method (cGANs) to a multimodal context.
The paper tackled emotion detection by proposing a multimodal approach using Conditional Generative Adversarial Networks (cGANs) to integrate text, audio, and facial expressions, resulting in significant improvements in recognition performance compared to baseline models.
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.