Automatic reading diagnosis systems can benefit both teachers for more
efficient scoring of reading exercises and students for accessing reading
exercises with feedback more easily. However, there are limited studies on
Automatic Speech Recognition (ASR) for child speech in languages other than
English, and limited research on ASR-based reading diagnosis systems. This
study investigates how efficiently state-of-the-art (SOTA) pretrained ASR
models recognize Dutch native children speech and manage to detect reading
miscues. We found that Hubert Large finetuned on Dutch speech achieves SOTA
phoneme-level child speech recognition (PER at 23.1\%), while Whisper (Faster
Whisper Large-v2) achieves SOTA word-level performance (WER at 9.8\%). Our
findings suggest that Wav2Vec2 Large and Whisper are the two best ASR models
for reading miscue detection. Specifically, Wav2Vec2 Large shows the highest
recall at 0.83, whereas Whisper exhibits the highest precision at 0.52 and an
F1 score of 0.52.

本研究探讨了现有的最先进预训练自动语音识别（ASR）模型在识别荷兰语为母语的儿童语音和检测阅读错误方面的效果，发现 Hubert Large 在荷兰语儿童语音识别方面达到最佳表现（23.1% 的音位级错误率），而 Whisper（Faster Whisper Large-v2）则在词级表现最佳（9.8% 的词错误率）。此外，Wav2Vec2 Large 和 Whisper 是用于阅读错误检测的两个最佳 ASR 模型，其中 Wav2Vec2 Large 的召回率最高（0.83），而 Whisper 的精确度和 F1 得分均为 0.52。