基于CNN的在线多媒体英语教学情感交互研究  

A Study of Emotional Interaction in Online Multimedia English Teaching Based on CNN

作　　者：梁珊[1] Liang Shan(Academic Affairs Office,Shaanxi College of Communication Technology,Xi’an 710000,China)

机构地区：[1]陕西交通职业技术学院教务处,西安710000

出　　处：《兵工自动化》2024年第9期36-39,共4页Ordnance Industry Automation

摘　　要：针对多媒体英语教学中情感缺失的问题,提出一种基于人脸表情识别的智能网络教学系统模型。应用主成分分析(principal component analysis,PCA)提取在线学习者视频中面部表情的重要特征帧;基于CNN架构的面部情绪识别网络判断和理解学习者的情绪状态,根据学习者的具体情绪状态给予相应的情绪鼓励或情绪补偿策略。仿真结果表明:与VGG16和ResNet50比较,该算法平均检测率为78.28%,平均识别准确率为81.78%,性能明显较优。In order to solve the problem of emotion absence in multimedia English teaching,an intelligent network teaching system model based on facial expression recognition is proposed.Principal component analysis(PCA)is applied to extract the important feature frames of facial expressions in online learner videos;the facial emotion recognition network based on CNN architecture judges and understands the emotional state of the learner,and gives corresponding emotional encouragement or emotional compensation strategies according to the specific emotional state of the learner.Simulation results show that the average detection rate of the proposed algorithm is 78.28%,and the average recognition accuracy is 81.78%,compared with VGG16 and ResNet50.

关 键 词：多媒体英语教学 情感 人脸表情识别 卷积神经网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]