mlsanityData to 7 Oct 2026

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

arXiv 2604.19801 ↗First posted 10 Apr 2026eess.AS cs.AI cs.CLcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 10 Apr 2026.

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References · 13

  1. 2020Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognitioncited by 35
  2. 2023Adaptation of Whisper models to child speech recognitioncited by 25
  3. 2021An evaluation of word-level confidence estimation for end-to-end automatic speech recognitioncited by 18
  4. 2021End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Studycited by 17
  5. 2024Improving child speech recognition with augmented child-like speechcited by 7
  6. 2025Evaluating ASR Confidence Scores for Automated Error Detection in User-Assisted Correction Interfacescited by 6
  7. 2025Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AIcited by 5
  8. 2025Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN Corpuscited by 5
  9. 2024Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free Metriccited by 4
  10. 2017Automatic Quality Estimation for ASR System Combinationcited by 3
  11. 2025Improving Child Speech Recognition and Reading Mistake Detection by Using Promptscited by 3
  12. 2023An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graderscited by 2
1 more
  1. 2025Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Databasecited by 2

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