• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Telecommunications and Signal Processing (TSP), 2012 35th International Conference on >Computational emotion recognition using multimodal physiological signals: Elicited using Japanese kanji words
【24h】

Computational emotion recognition using multimodal physiological signals: Elicited using Japanese kanji words

机译：使用多模态生理信号进行计算性情感识别：使用日语汉字单词选出

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper investigates computational emotion recognition using multimodal physiological signals. Four physiological signs - plethysmogram, skin conductance change, respiration rate and skin temperature - are measured to evaluate three emotions: positive, negative and neutral. Psychophysical experiments are conducted using Japanese kanji words in order to excite emotions in subjects so as to elicit physiological signals. For computational emotion recognition, machine-learning approaches, such as multilayer neural networks, support vector machines, decision trees and random forests, are used to design emotion recognition systems and their characteristics are investigated. In computational experiments conducted for recognising emotions, support vector machines equipped with a Gaussian kernel function attain a maximum averaged recognition rate of around 40% for all three emotions and around 56% for two emotions (positive and negative). The results obtained in this study shows that using multimodal physiological signals with a machine-learning approach is feasible and suited for computational emotion recognition.
机译：本文研究使用多模式生理信号进行计算性情绪识别。测量了四个生理信号-体积描记图，皮肤电导变化，呼吸频率和皮肤温度-以评估三种情绪：积极，消极和中性。使用日语汉字单词进行心理物理实验，以激发受试者的情绪，从而激发出生理信号。对于计算情感识别，使用了诸如多层神经网络，支持向量机，决策树和随机森林之类的机器学习方法来设计情感识别系统，并研究了它们的特性。在进行识别情绪的计算实验中，配备有高斯核函数的支持向量机对所有三种情绪的最大平均识别率分别为40％和对两种情绪（正负）的平均识别率分别约为56％。在这项研究中获得的结果表明，将多模式生理信号与机器学习方法结合使用是可行的，并且适合于计算情感识别。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号