论文标题
用于集体活动检测的暹罗神经网络
Siamese Neural Networks for Class Activity Detection
论文作者
论文摘要
课堂活动检测（CAD）旨在准确识别教室中的扬声器角色（教师或学生）。 CAD解决方案可帮助教师就其教学说明获得即时反馈。但是，CAD非常具有挑战性，因为（1）课堂对话包含教师和学生之间的许多对话转折重叠； （2）对于不同的老师和学生，CAD模型需要足够通用； （3）课堂录音可能非常嘈杂且质量低。在这项工作中，我们通过建立一个暹罗神经框架来应对上述挑战，以自动从课堂录音中识别教师和学生的话语。提出的模型对现实世界的教育数据集进行了评估。结果表明，（1）我们的方法在在线和离线课堂环境中的预测任务上都是优越的； （2）我们的框架对新教师具有稳健性和泛化能力（即，教师从未出现在培训数据中）。
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on their pedagogical instructions. However, CAD is very challenging because (1) classroom conversations contain many conversational turn-taking overlaps between teachers and students; (2) the CAD model needs to be generalized well enough for different teachers and students; and (3) classroom recordings may be very noisy and low-quality. In this work, we address the above challenges by building a Siamese neural framework to automatically identify teacher and student utterances from classroom recordings. The proposed model is evaluated on real-world educational datasets. The results demonstrate that (1) our approach is superior on the prediction tasks for both online and offline classroom environments; and (2) our framework exhibits robustness and generalization ability on new teachers (i.e., teachers never appear in training data).
