用于阅读诊断的口语阅读准确性自动评估
Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics
AI总结：
本研究评估了六个基于ASR的系统使用Kaldi和Whisper自动评估荷兰语口语阅读准确性的效果，发现包含阅读错误的语言模型系统表现最佳，与人类评估达到了实质性一致。
摘要：
使用自动语音识别（ASR）进行阅读流利度自动评估，对于早期发现阅读困难及后续及时干预具有巨大潜力。需要精确的评估工具，尤其是针对英语以外的语言。在本研究中，我们评估了六个基于ASR的最先进系统，这些系统使用Kaldi和Whisper自动评估荷兰语口语阅读准确性。结果显示，我们最成功的系统与人类评估达到了实质性一致（MCC = .63）。同一系统在强制解码置信度分数与单词正确率之间达到了最高相关性（r = .45）。该系统的语言模型（LM）由测试数据的手工正字转录和阅读提示组成，这表明在LM中包含阅读错误能提高评估性能。我们讨论了这些发现对开发自动评估系统的影响，并确定了未来研究的可能方向。
英文摘要：
Automatic assessment of reading fluency using automatic speech recognition (ASR) holds great potential for early detection of reading difficulties and subsequent timely intervention. Precise assessment tools are required, especially for languages other than English. In this study, we evaluate six state-of-the-art ASR-based systems for automatically assessing Dutch oral reading accuracy using Kaldi and Whisper. Results show our most successful system reached substantial agreement with human evaluations (MCC = .63). The same system reached the highest correlation between forced decoding confidence scores and word correctness (r = .45). This system's language model (LM) consisted of manual orthographic transcriptions and reading prompts of the test data, which shows that including reading errors in the LM improves assessment performance. We discuss the implications for developing automatic assessment systems and identify possible avenues of future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
