人类情绪识别的生成技术:范围审查

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Information Fusion Pub Date : 2024-10-29 DOI:10.1016/j.inffus.2024.102753
Fei Ma , Yucheng Yuan , Yifan Xie , Hongwei Ren , Ivan Liu , Ying He , Fuji Ren , Fei Richard Yu , Shiguang Ni
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引用次数: 0

摘要

情感计算站在人工智能(AI)的最前沿,试图赋予机器理解和回应人类情感的能力。情感识别是这一领域的核心,它致力于从语音、面部图像、文本和生理信号等不同模式中识别和解释人类的情感状态。近年来,包括自动编码器、生成对抗网络、扩散模型和大型语言模型在内的生成模型取得了重要进展。这些模型具有强大的数据生成能力,是推动情感识别的重要工具。然而,迄今为止,对情感识别的生成技术进行系统回顾的工作仍然很少。本调查旨在通过对截至 2024 年 6 月的 330 多篇研究论文进行全面分析,弥补现有文献的不足。具体来说,本调查将首先介绍不同生成模型的数学原理和常用数据集。然后,通过分类法,深入分析生成技术如何从数据增强、特征提取、半监督学习、跨域等几个方面解决基于不同模态的情感识别问题。最后,综述将概述未来的研究方向,强调生成模型在推动情感识别领域发展和提高人工智能系统情感智能方面的潜力。
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Generative technology for human emotion recognition: A scoping review
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.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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