Summary of Generative Technology For Human Emotion Recognition: a Scope Review, by Fei Ma et al.
Generative Technology for Human Emotion Recognition: A Scope Review
by Fei Ma, Yucheng Yuan, Yifan Xie, Hongwei Ren, Ivan Liu, Ying He, Fuji Ren, Fei Richard Yu, Shiguang Ni
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In a breakthrough in artificial intelligence (AI), researchers have made significant progress in generative models that can comprehend and respond to human emotions. This has led to advancements in emotion recognition, which aims to identify and interpret human emotional states from various modalities like speech, facial images, text, and physiological signals. Generative models such as Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model have emerged as crucial tools in advancing emotion recognition. To bridge the gaps in existing literature, a comprehensive analysis of over 320 research papers is conducted until June 2024. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Affective computing aims to make machines understand human emotions. Emotion recognition tries to identify emotional states from different sources like speech and facial expressions. Generative models can generate data and are crucial for emotion recognition. This survey reviews generative technology for emotion recognition, looking at math principles, datasets, and how generative techniques work. It also explores future research directions and how generative models can improve AI systems’ emotional intelligence. |
Keywords
» Artificial intelligence » Autoencoder » Diffusion model » Generative adversarial network » Large language model
