深度学习情感识别方法

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Weidong Xiao, Wenjin Tan, Naixue Xiong, Ce Yang, Lin Chen, Rui Xie
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引用次数: 0

摘要

情感识别是指通过计算机技术主动分析人类情感的过程,已成为现代社会的重要组成部分。传统的情感识别主要是基于单一的信息源,如文本、语音、视频等,从中提取情感特征进行分类或回归,从而识别人类的情感。随着人工智能技术的发展,多模态情感识别逐渐得到广泛应用。它结合了两种或两种以上类型的信息,如文本、语音和视觉信息,以不同的方式来分析情绪。多模态情绪识别在理解情绪方面远优于单模态。本文主要分析了情感分析技术。首先介绍了情绪识别的基本概念和研究现状。然后,我们介绍了情绪识别的主要类型,并详细描述了在此过程中使用的各种方法。最后,我们讨论了情感识别的挑战和未来的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Emotion Recognition Method
Emotion recognition refers to the process of actively analyzing human emotions through computer technology, and it has become an important part of modern society. Traditional emotion recognition is mainly based on a single information source, such as text, speech, video, etc., from which emotional features are extracted for classification or regression to recognize human emotions. With the development of artificial intelligence technology, multimodal emotion recognition is gradually becoming widely used. It combines two or more types of information, such as text, speech, and visual information, in different ways to analyze emotions. Multimodal emotion recognition is far superior to a single modality in understanding emotions. This article mainly analyzes the technology of emotion analysis. Firstly, we introduce the basic concepts and research status of emotion recognition. Then, we introduce the main types of emotion recognition and describe various methods used in the process in detail. Finally, we discuss the challenges and future developments of emotion recognition.
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来源期刊
Journal of Cloud Computing-Advances Systems and Applications
Journal of Cloud Computing-Advances Systems and Applications Computer Science-Computer Networks and Communications
CiteScore
6.80
自引率
7.50%
发文量
76
审稿时长
75 days
期刊介绍: The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Principally, articles will address topics that are core to Cloud Computing, focusing on the Cloud applications, the Cloud systems, and the advances that will lead to the Clouds of the future. Comprehensive review and survey articles that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.
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