# Reading Miscue Detection in Primary School through Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2406.07060 · HTML: https://mlsanity.com/paper/2406.07060/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study](https://mlsanity.com/paper/2102.09918/) (2021)
- [Quantifying Language Variation Acoustically with Few Resources](https://mlsanity.com/paper/2205.02694/) (2022)
- [Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource](https://mlsanity.com/paper/1607.00225/) (2016)
- [Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics](https://mlsanity.com/paper/2306.03444/) (2023)

## Built on by (most cited first, 1 of 1)

- [Transcribing Children's Speech: ASR Performance and Obtaining Reliable Orthographic Transcriptions](https://mlsanity.com/paper/2605.28833/) (2026)

## Abstract (arXiv)

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.