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.

翻译：近年来，将自动语音识别（ASR）技术应用于阅读练习程序的兴趣日益增长。在先前研究中，我们开发了一款基于ASR的荷兰语阅读辅导应用程序，旨在为正在学习阅读的一年级学生提供即时反馈。结果表明，该软件有助于学生在阅读准确性和流利度方面取得进步，这证实了ASR在该阅读阶段具有潜力。在本研究中，我们使用现有语料库（JASMIN）中的儿童语音数据开发了两个新的ASR系统，并将结果与先前研究进行对比。通过Cohen's Kappa系数、马修斯相关系数（MCC）、精确率、召回率和F值等评估指标，我们基于人工转录的单词级标注分析了ASR系统的正确/错误分类情况。观察到新开发的ASR系统在与人工判断的一致性和正确拒绝（CR）方面均有所改进。不同阅读任务和词类下ASR系统的准确率存在差异。结果表明，在当前配置下，对孤立单词进行分类较为困难。我们讨论了这些结果、改进系统的可能途径及未来研究方向。