In this work, we present the first study addressing automatic speech recognition (ASR) for children in an online learning setting. This is particularly important for both child-centric applications and the privacy protection of minors, where training models with sequentially arriving data is critical. The conventional approach of model fine-tuning often suffers from catastrophic forgetting. To tackle this issue, we explore two established techniques: elastic weight consolidation (EWC) and synaptic intelligence (SI). Using a custom protocol on the MyST corpus, tailored to the online learning setting, we achieve relative word error rate (WER) reductions of 5.21% with EWC and 4.36% with SI, compared to the fine-tuning baseline.
@article{arxiv.2505.20216,
title = {Continuous Learning for Children's ASR: Overcoming Catastrophic Forgetting with Elastic Weight Consolidation and Synaptic Intelligence},
author = {Edem Ahadzi and Vishwanath Pratap Singh and Tomi Kinnunen and Ville Hautamaki},
journal= {arXiv preprint arXiv:2505.20216},
year = {2025}
}