多模态维度情感预测综述
A Survey of Dimensional Emotion Prediction by Multimodal Cues
李霞 1卢官明 2闫静杰 2张正言3
作者信息
- 1. 南京邮电大学通信与信息工程学院 南京 210003;安徽工业大学数理学院 马鞍山 243000
- 2. 南京邮电大学通信与信息工程学院 南京 210003
- 3. 南京邮电大学通信与信息工程学院 南京 210003;江苏科技大学电子信息学院 镇江 212003
- 折叠
摘要
维度情感模型通过几个取值连续的维度 (如唤醒维、效价维、支配维等) 将情感刻画为一个多维信号.与传统的离散情感模型相比, 具有表示情感的范围广、能描述情感的演变过程等优点, 近年来受到越来越多情感识别研究者的关注.多模态维度情感预测是一项复杂的工程, 预测性能受所使用的模态、每个模态的特征提取、信息融合技术、标注人员的标注误差等多方面影响.为了提高多模态维度情感预测的性能, 研究者在各个方面都做出了不懈努力.本文综述了维度情感的概念、标注, 维度情感预测的性能评价指标以及多模态维度情感预测的研究现状, 对比和分析了各种因素对多模态维度情感预测性能的影响, 并总结出多模态维度情感预测面临的挑战及发展趋势.
Abstract
The dimensional emotion model characterizes emotion as a signal in a multi-dimensional space spanned by several continuously valued dimensions (such as arousal, valence, and dominance). Compared with the discrete emotion model, it has the advantages that it can distinguish subtle difference of emotion, can represent evolution of emotion, etc. So the dimensional emotion model has been paid more and more attention in recent years. Dimensional emotion prediction from multimodal cues is a complex task, the prediction performance is influenced by such as modalities used, features extracted from each modality, information fusion technique, annotation errors. In order to improve multimodal dimensional emotion prediction performance, researchers have made persistent efforts in all aspects. In the paper, concept and annotation of dimensional emotion, performance evaluation criteria of dimensional emotion prediction, and research status of multimodal dimensional emotion prediction are reviewed; influences of various factors on emotion prediction performance are analyzed; challenge and development trend of multimodal dimensional emotion prediction are summarized.
关键词
情感识别/情感预测/维度情感模型/离散情感模型/信息融合/特征提取Key words
Emotion recognition/emotion prediction/dimensional emotion model/discrete emotion model/information fusion/feature extraction引用本文复制引用
基金项目
国家自然科学基金(61501249)
国家自然科学基金(61071167)
江苏省重点研发计划项目(BE2016775)
江苏省自然科学基金(BK20150855)
江苏省研究生创新项目(KYLX15 0827)
江苏省研究生创新项目(KYLX16 0660)
出版年
2018
国家科技期刊平台
NSTL
维普
万方数据