# Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts

arXiv: https://arxiv.org/abs/2506.11079 · HTML: https://mlsanity.com/paper/2506.11079/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 4 Jun 2025.

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

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks](https://mlsanity.com/paper/2406.13469/) (2024)
- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [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)
- [Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of Whisper](https://mlsanity.com/paper/2406.05806/) (2024)
- [Improving child speech recognition with augmented child-like speech](https://mlsanity.com/paper/2406.10284/) (2024)
- [ProGRes: Prompted Generative Rescoring on ASR n-Best](https://mlsanity.com/paper/2409.00217/) (2024)
- [Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics](https://mlsanity.com/paper/2306.03444/) (2023)

## Built on by (most cited first, 3 of 3)

- [Utterance-Level Methods for Identifying Reliable ASR-Output for Child Speech](https://mlsanity.com/paper/2604.19801/) (2026)
- [Transcribing Children's Speech: ASR Performance and Obtaining Reliable Orthographic Transcriptions](https://mlsanity.com/paper/2605.28833/) (2026)
- [No Detectable Change in Side-Level WER from Prompt-Level Context: A Preregistered Ablation on a Production Oral-History Corpus](https://mlsanity.com/paper/2608.28875/) (2026)

## Abstract (arXiv)

Automatic reading aloud evaluation can provide valuable support to teachers by enabling more efficient scoring of reading exercises. However, research on reading evaluation systems and applications remains limited. We present a novel multimodal approach that leverages audio and knowledge from text resources. In particular, we explored the potential of using Whisper and instruction-tuned large language models (LLMs) with prompts to improve transcriptions for child speech recognition, as well as their effectiveness in downstream reading mistake detection. Our results demonstrate the effectiveness of prompting Whisper and prompting LLM, compared to the baseline Whisper model without prompting. The best performing system achieved state-of-the-art recognition performance in Dutch child read speech, with a word error rate (WER) of 5.1%, improving the baseline WER of 9.4%. Furthermore, it significantly improved reading mistake detection, increasing the F1 score from 0.39 to 0.73.