# Utterance-Level Methods for Identifying Reliable ASR-Output for Child Speech

arXiv: https://arxiv.org/abs/2604.19801 · HTML: https://mlsanity.com/paper/2604.19801/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition](https://mlsanity.com/paper/2010.11428/) (2020)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [An evaluation of word-level confidence estimation for end-to-end automatic speech recognition](https://mlsanity.com/paper/2101.05525/) (2021)
- [End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study](https://mlsanity.com/paper/2102.09918/) (2021)
- [Improving child speech recognition with augmented child-like speech](https://mlsanity.com/paper/2406.10284/) (2024)
- [Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI](https://mlsanity.com/paper/2501.06129/) (2025)
- [Evaluating ASR Confidence Scores for Automated Error Detection in User-Assisted Correction Interfaces](https://mlsanity.com/paper/2503.15124/) (2025)
- [Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN Corpus](https://mlsanity.com/paper/2502.17284/) (2025)
- [Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free Metric](https://mlsanity.com/paper/2401.11268/) (2024)
- [Automatic Quality Estimation for ASR System Combination](https://mlsanity.com/paper/1706.07238/) (2017)
- [Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts](https://mlsanity.com/paper/2506.11079/) (2025)
- [An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graders](https://mlsanity.com/paper/2306.04190/) (2023)
- [Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database](https://mlsanity.com/paper/2508.13060/) (2025)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) is increasingly used in applications involving child speech, such as language learning and literacy acquisition. However, the effectiveness of such applications is limited by high ASR error rates. The negative effects can be mitigated by identifying in advance which ASR-outputs are reliable. This work aims to develop two novel approaches for selecting reliable ASR-output at the utterance level, one for selecting reliable read speech and one for dialogue speech material. Evaluations were done on an English and a Dutch dataset, each with a baseline and finetuned model. The results show that utterance-level selection methods for identifying reliably transcribed speech recordings have high precision for the best strategy (P > 97.4) for both read speech and dialogue material, for both languages. Using the current optimal strategy allows 21.0% to 55.9% of dialogue/read speech datasets to be automatically selected with low (UER of < 2.6) error rates.