IERI: Integrating Speech and User Modeling in a Reading Tutor that Listens

交互式系统如何通过倾听用户的声音来更好地模拟用户？ 相反，系统如何通过利用用户模型来更好地倾听？ 我们建议在跨学科挑战问题的背景下调查这些问题：开发一个儿童口语阅读的计算学生模型-也就是说，一个可以指导导师的学生个人知识，行为和学习的模型。 这项工作将整合和扩展语音技术，认知心理学，用户建模和智能导师的方法。拟议的工作将借鉴，促进，测试和完善创新的可扩展性，技术支持的干预和独特的研究平台开发与以前的NSF支持：一个自动化的阅读辅导员，在电脑屏幕上显示故事，使用语音识别器来听孩子大声朗读，用口头和图形帮助来回应，帮助他们学习阅读。 它的倾听能力使学生的阅读进展的新颖的持续评估。截至2003年，阅读辅导员每天在9所学校的216台电脑上被数百名儿童使用。 拟议的工作将使用语音和其他有价值的数据捕获的阅读导师。 使用阅读导师的孩子在阅读理解和其他技能方面的进步比统计学上匹配的对照组要大得多。 然而，它目前的有效性是有限的，无法准确地听到和模拟学生。挑战是分析儿童的口语阅读和估计各种组成识字技能在一个足够细的粒度，以指导决策的智能导师，以配合学生个人或集体的教育需要。发展学生口语阅读模式的挑战，会带来一个问题-指导工作和量化其成功的驱动基础。 在阅读辅导员中不断验证更好的模型，既可以测试它们的准确性，也可以将它们立即用于改进识字辅导。 对照研究将调查阅读导师的连续版本为不同类型的学生增加了多少学习收益。 嵌入在阅读导师中的自动化实验将有助于分析哪些辅导行动在哪些情况下有帮助。拟议的研究结合了广泛的技术和直接的社会影响。 这项工作的预期技术贡献包括语音识别（特别是年轻人和不流利的说话者），用户建模，智能辅导，以及理解和其他识字技能的自动评估。 更广泛的影响包括在研究期间使用阅读导师的数千名儿童的识字率提高，以及此后更多的儿童。

How can interactive systems model their users better by listening to them? Conversely, how can systems listen better by exploiting user models? We propose to investigate these questions in the context of a inter-disciplinary challenge problem: developing a computational student model of children's oral reading -- that is, a model of individual student knowledge, behavior, and learning that can guide a tutor. This work will integrate and extend methods from speech technology, cognitive psychology, user modeling, and intelligent tutors.The proposed work will draw on, contribute to, and test and refine the scalability of an innovative, technology-enabled intervention and unique research platform developed with previous NSF support: an automated Reading Tutor that displays stories on a computer screen, uses a speech recognizer to listen to children read aloud, responds with spoken and graphical assistance, and helps them learn to read. Its ability to listen enables novel continuous assessments of students' reading progress. As of 2003, the Reading Tutor was used daily by hundreds of children on 216 computers in nine schools. The proposed work will use speech and other valuable data captured by the Reading Tutor. Children who use the Reading Tutor have improved significantly more in reading comprehension and other skills than statistically matched controls. However, its current effectiveness is limited by inability to accurately hear and model the student.The challenge is to analyze children's oral reading and estimate various component literacy skills at a sufficiently fine grain size to guide the decisions of an intelligent tutor, so as to adapt to students' individual or collective educational needs.The challenge of developing a student model of oral reading will provide a problem-driven basis for guiding the work and quantifying its success. Incorporating successively better models in the Reading Tutor will both test their accuracy and put them to immediate use in improved literacy tutoring. Controlled studies will investigate how much successive versions of the Reading Tutor increase learning gains for different types of students. Automated experiments embedded in the Reading Tutor will help analyze which tutorial actions help in which cases.The proposed research combines broad technical and direct societal impact. Expected technical contributions of this work include advances in speech recognition (especially for young and disfluent speakers), user modeling, intelligent tutoring, and automated assessment of comprehension and other literacy skills. Broader impacts include improved literacy for thousands of children who use the Reading Tutor during the study, and many more thereafter.