Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 330 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.

中文翻译：

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用于人类情感识别的生成式技术：范围界定审查


情感计算站在人工智能 （AI） 的前沿，旨在赋予机器理解和响应人类情感的能力。该领域的核心是情绪识别，它致力于从不同模式（如语音、面部图像、文本和生理信号）中识别和解释人类的情绪状态。近年来，生成模型取得了重要进展，包括自动编码器、生成对抗网络、扩散模型和大型语言模型。这些模型具有强大的数据生成能力，成为推进情绪识别的关键工具。然而，到目前为止，仍然缺乏系统性的努力来审查用于情绪识别的生成技术。这项调查旨在通过对 2024 年 6 月之前的 330 多篇研究论文进行全面分析来弥合现有文献中的差距。具体来说，本次调查将首先介绍不同生成模型的数学原理和常用的数据集。随后，通过分类法，它将从几个方面深入分析生成技术如何基于不同的模态解决情感识别问题，包括数据增强、特征提取、半监督学习、跨域等。最后，综述将概述未来的研究方向，强调生成模型在推进情绪识别和增强 AI 系统情商方面的潜力。