With advancements in multimodal communication technologies, remote learning environments such as, distance universities are increasing. Remote learning typically happens asynchronously. As a consequence, unlike face-to-face in-person classroom teaching, this lacks availability of sufficient emotional cues for making learning a pleasant experience. Motivated by advances made in the paralinguistic speech processing community on emotion prediction, in this paper we explore use of speech for sensing students' emotions by building upon speech-based self-control tasks developed to aid effective remote learning. More precisely, we investigate: (a) whether speech acquired through self-control tasks exhibit perceptible variation along valence, arousal, and dominance dimensions? and (b) whether those dimensional emotion variations can be automatically predicted? We address these two research questions by developing a dataset containing spontaneous monologue speech acquired as open responses to self-control tasks and by carrying out subjective listener evaluations and automatic dimensional emotion prediction studies on that dataset. Our investigations indicate that speech-based self-control tasks can be a means to sense student emotion in remote learning environment. This opens potential venues to seamlessly integrate paralinguistic speech processing technologies in the remote learning loop for enhancing learning experiences through instructional design and feedback generation.

翻译：随着多模态通信技术的进步，远程学习环境（如远程大学）日益增多。远程学习通常以异步方式进行，因此，与面对面课堂教学不同，它缺乏足够的情绪线索来使学习成为愉快的体验。受副语言语音处理领域在情绪预测方面取得进展的启发，本文探索了利用语音感知学生情绪的方法，即基于为促进有效远程学习而设计的语音自控任务。具体而言，我们研究了：（a）通过自控任务获取的语音是否在效价、唤醒度和支配维度上表现出可感知的变异？（b）这些维度上的情绪变化是否能被自动预测？为解决这两个研究问题，我们开发了一个数据集，其中包含作为自控任务开放式回答采集的自发独白语音，并对该数据集开展了主观听者评估和自动维度情绪预测研究。我们的研究表明，基于语音的自控任务可以成为在远程学习环境中感知学生情绪的一种手段。这为将副语言语音处理技术无缝集成到远程学习循环中开辟了潜在途径，从而通过教学设计及反馈生成来提升学习体验。