  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.
