标题： 基于语音识别的阅读教学辅导系统：如何优化对一年级学生的反馈 显示英文标题

标题： An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graders

摘要： 近年来，将自动语音识别（ASR）应用于阅读练习应用的兴趣正在增长。 在以前的研究中，我们介绍了一种基于ASR的荷兰阅读导师应用程序，该程序旨在为正在学习阅读的一年级学生提供即时反馈。 我们发现 在阅读过程的这一阶段，ASR具有潜力，因为结果表明，学生通过使用软件在阅读准确性和流利度方面取得了进步。 在当前的研究中，我们使用了一个现有语料库（JASMIN）中的儿童语音来开发两个新的ASR系统，并将结果与之前的研究结果进行了比较。 我们通过Cohen's Kappa、Matthews相关系数（MCC）、精确率、召回率和F度量等评估指标，使用人工转录文本在单词级别上分析了ASR系统的正确/错误分类。 我们观察到，新开发的ASR系统在与基于人类的判断的一致性以及正确拒绝（CR）方面有所改善。 ASR系统的准确性因不同的阅读任务和单词类型而异。 我们的结果表明，在当前配置下，对孤立单词进行分类是困难的。 我们讨论了这些结果，改进我们系统的可能方法以及未来研究的方向。 显示英文摘要

摘要： The interest in employing automatic speech recognition (ASR) in applications for reading practice has been growing in recent years. In a previous study, we presented an ASR-based Dutch reading tutor application that was developed to provide instantaneous feedback to first-graders learning to read. We saw that ASR has potential at this stage of the reading process, as the results suggested that pupils made progress in reading accuracy and fluency by using the software. In the current study, we used children's speech from an existing corpus (JASMIN) to develop two new ASR systems, and compared the results to those of the previous study. We analyze correct/incorrect classification of the ASR systems using human transcripts at word level, by means of evaluation measures such as Cohen's Kappa, Matthews Correlation Coefficient (MCC), precision, recall and F-measures. We observe improvements for the newly developed ASR systems regarding the agreement with human-based judgment and correct rejection (CR). The accuracy of the ASR systems varies for different reading tasks and word types. Our results suggest that, in the current configuration, it is difficult to classify isolated words. We discuss these results, possible ways to improve our systems and avenues for future research.