# End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study

arXiv: https://arxiv.org/abs/2102.09918 · HTML: https://mlsanity.com/paper/2102.09918/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2021.

## Built on by (most cited first, 18 of 18)

- [My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speech](https://mlsanity.com/paper/2309.13347/) (2023)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning](https://mlsanity.com/paper/2503.04710/) (2025)
- [Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech Representations](https://mlsanity.com/paper/2211.07769/) (2022)
- [FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech Data](https://mlsanity.com/paper/2406.17926/) (2024)
- [Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts](https://mlsanity.com/paper/2506.11079/) (2025)
- [A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognition](https://mlsanity.com/paper/2311.04936/) (2023)
- [Large Language Models based ASR Error Correction for Child Conversations](https://mlsanity.com/paper/2505.16212/) (2025)
- [Reading Miscue Detection in Primary School through Automatic Speech Recognition](https://mlsanity.com/paper/2406.07060/) (2024)
- [Who Said What WSW 2.0? Enhanced Automated Analysis of Preschool Classroom Speech](https://mlsanity.com/paper/2505.09972/) (2025)
- [Can Layer-wise SSL Features Improve Zero-Shot ASR Performance for Children's Speech?](https://mlsanity.com/paper/2508.21225/) (2025)
- [Error-preserving Automatic Speech Recognition of Young English Learners' Language](https://mlsanity.com/paper/2406.03235/) (2024)
- [Designing the virtual CAT: A digital tool for algorithmic thinking assessment in compulsory education](https://mlsanity.com/paper/2408.01263/) (2024)
- [Examining Test-Time Adaptation for Personalized Child Speech Recognition](https://mlsanity.com/paper/2409.13095/) (2024)
- [Causal Structure Discovery for Error Diagnostics of Children's ASR](https://mlsanity.com/paper/2506.00402/) (2025)
- [Utterance-Level Methods for Identifying Reliable ASR-Output for Child Speech](https://mlsanity.com/paper/2604.19801/) (2026)
- [Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results](https://mlsanity.com/paper/2607.19049/) (2026)
- [Child ASR Adaptation with Adult Retention: An Empirical Study](https://mlsanity.com/paper/2610.08827/) (2026)

## Abstract (arXiv)

A key desiderata for inclusive and accessible speech recognition technology is ensuring its robust performance to children's speech. Notably, this includes the rapidly advancing neural network based end-to-end speech recognition systems. Children speech recognition is more challenging due to the larger intra-inter speaker variability in terms of acoustic and linguistic characteristics compared to adult speech. Furthermore, the lack of adequate and appropriate children speech resources adds to the challenge of designing robust end-to-end neural architectures. This study provides a critical assessment of automatic children speech recognition through an empirical study of contemporary state-of-the-art end-to-end speech recognition systems. Insights are provided on the aspects of training data requirements, adaptation on children data, and the effect of children age, utterance lengths, different architectures and loss functions for end-to-end systems and role of language models on the speech recognition performance.