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.

翻译：利用自动语音识别（ASR）自动评估朗读流畅度，对于早期检测阅读困难及后续及时干预具有巨大潜力。针对非英语语言，尤其需要精准的评估工具。本研究评估了六种基于Kaldi和Whisper的最先进ASR系统，用于自动评估荷兰语口头朗读的准确性。结果表明，我们最成功的系统与人工评估达到了较高的一致性（MCC = .63）。该系统在强制解码置信度分数与词语正确性之间达到了最高的相关性（r = .45）。该系统的语言模型（LM）由测试数据的手工正字法转录文本和朗读提示构成，这表明将阅读错误纳入LM可提升评估性能。我们讨论了开发自动评估系统的启示，并指出了未来可能的研究方向。