Emotion Detection Using Conditional Generative Adversarial Networks (cGAN): A Deep Learning Approach

TL;DR

This paper introduces a multimodal emotion detection method using cGANs that synthesizes emotion data across text, audio, and facial expressions, significantly improving recognition accuracy.

Contribution

It proposes a novel cGAN-based multimodal framework for emotion detection, enhancing data synthesis and classification performance over traditional methods.

Findings

Improved emotion recognition accuracy with the proposed model

Effective synthesis of emotion-rich data across modalities

Demonstrated potential for advanced human-computer interaction

Abstract

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.