Scholarlink

<p>Teacher emotion recognition (TER) has a significant impact on student engagement, classroom atmosphere, and teaching quality, which is a research hotspot in the smart education area. However, existing studies lack high-quality multimodal datasets and neglect common and discriminative features of multimodal data in emotion expression. To address these challenges, this research constructs a multimodal TER dataset suitable for real classroom teaching scenarios. TER dataset contains a total of 102 lessons and 2,170 video segments from multiple educational stages and subjects, innovatively labelled with emotional tags that characterize teacher–student interactions, such as satisfaction and questions. To explore the characteristics of multimodal data in emotion expression, this research proposes an emotion dual-space network (EDSN) that establishes an emotion commonality space construction (ECSC) module and an emotion discrimination space construction (EDSC) module. Specifically, the EDSN utilizes central moment differences to measure the similarity to assess the correlation between multiple modalities within the emotion commonality space. On this basis, the gradient reversal layer and orthogonal projection are further utilized to construct the EDSC to extract unique emotional information and remove redundant information from each modality. Experimental results demonstrate that the EDSN achieves an accuracy of 0.770 and a weighted F1 score of 0.769 on the TER dataset, outperforming other comparative models.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Emotion Dual-Space Network Based on Common and Discriminative Features for Multimodal Teacher Emotion Recognition

  • 期刊论文

  • 发表日期：

    2025年7月4日

查看全文
  • Ting Cai,
  • Shengsong Wang,
  • Jing Wang,
  • Yu Xiong,
  • Long Liu

摘要

Teacher emotion recognition (TER) has a significant impact on student engagement, classroom atmosphere, and teaching quality, which is a research hotspot in the smart education area. However, existing studies lack high-quality multimodal datasets and neglect common and discriminative features of multimodal data in emotion expression. To address these challenges, this research constructs a multimodal TER dataset suitable for real classroom teaching scenarios. TER dataset contains a total of 102 lessons and 2,170 video segments from multiple educational stages and subjects, innovatively labelled with emotional tags that characterize teacher–student interactions, such as satisfaction and questions. To explore the characteristics of multimodal data in emotion expression, this research proposes an emotion dual-space network (EDSN) that establishes an emotion commonality space construction (ECSC) module and an emotion discrimination space construction (EDSC) module. Specifically, the EDSN utilizes central moment differences to measure the similarity to assess the correlation between multiple modalities within the emotion commonality space. On this basis, the gradient reversal layer and orthogonal projection are further utilized to construct the EDSC to extract unique emotional information and remove redundant information from each modality. Experimental results demonstrate that the EDSN achieves an accuracy of 0.770 and a weighted F1 score of 0.769 on the TER dataset, outperforming other comparative models.