Collaborative Research: Monitoring Student State in Tutorial Spoken Dialogue

协作研究：在教程口语对话中监控学生状态

• 批准号: 0328431
• 负责人: Diane Litman
• 金额: $ 42万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0328431&HistoricalAwards=false
• 关键词: Collaborative; Research; Monitoring; Student; State

This research investigates the feasibility and utility of monitoring student emotions in spoken dialogue tutorial systems. While human tutors respond to both the content of student utterances and underlying perceived emotions, most tutorial dialogue systems cannot detect student emotions, and furthermore are text-based, which may limit their success at emotion prediction. While there has been increasing interest in identifying problematic emotions (e.g. frustration, anger) in spoken dialogue applications such as call centers, little work has addressed the tutorial domain.The PIs are investigating the use of lexical, syntactic, dialogue, prosodic and acoustic cues to enable a computer tutor to automatically predict and respond to student emotions. The research is being performed in the context of ITSPOKE, a speech-based tutoring dialogue system for conceptual physics. The PIs are recording students interacting with ITSPOKE, manually annotating student emotions in these as well as in human-human dialogues, identifying linguistic and paralinguistic cues to the annotations, and using machine learning to predict emotions from potential cues. The PIs are then deriving strategies for adapting the system's tutoring based upon emotion identification.The major scientific contribution will be an understanding of whether cues available to spoken dialogue systems can be used to predict emotion, and ultimately to improve tutoring performance. The results will be of value to other applications that can benefit from monitoring emotional speech. Progress towards closing the performance gap between human tutors and current machine tutors will also expand the usefulness of current computer tutors.

这项研究调查了在口语对话教程系统中监测学生情绪的可行性和实用性。 尽管人类导师对学生的话语的内容和潜在的感知情绪做出了反应，但大多数教程对话系统无法检测到学生的情绪，此外，基于文本，这可能会限制他们在情感预测方面的成功。 尽管在呼叫中心等口语对话应用中识别有问题的情绪（例如挫败感，愤怒）的兴趣越来越多，但很少的工作已经解决了教程领域。 这项研究是在Itspoke的背景下进行的，这是一种基于语音的辅导对话系统，用于概念物理。 PI正在录制学生与Ispoke互动，手动注释学生的情绪以及人类对话中的对话，识别注释的语言和副语言提示，并使用机器学习来预测潜在的线索。 然后，PI是根据情感识别来提出调整系统辅导的策略。主要的科学贡献将是对是否可以使用可用于口语对话系统的提示来预测情感，并最终用于提高辅导性能。 结果对于可以从监控情绪语音中受益的其他应用程序具有价值。 缩小人辅导员与当前机器导师之间的性能差距的进展也将扩大当前计算机导师的实用性。