# Examining Test-Time Adaptation for Personalized Child Speech Recognition

arXiv: https://arxiv.org/abs/2409.13095 · HTML: https://mlsanity.com/paper/2409.13095/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults](https://mlsanity.com/paper/2309.07927/) (2023)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR](https://mlsanity.com/paper/2206.07931/) (2022)
- [Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition](https://mlsanity.com/paper/2203.14222/) (2022)
- [End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study](https://mlsanity.com/paper/2102.09918/) (2021)
- [Sparsely Shared LoRA on Whisper for Child Speech Recognition](https://mlsanity.com/paper/2309.11756/) (2023)
- [Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping](https://mlsanity.com/paper/2206.09396/) (2022)
- [Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings](https://mlsanity.com/paper/2405.13018/) (2024)
- [Senone-aware Adversarial Multi-task Training for Unsupervised Child to Adult Speech Adaptation](https://mlsanity.com/paper/2102.11488/) (2021)

## Abstract (arXiv)

Automatic speech recognition (ASR) models often experience performance degradation due to data domain shifts introduced at test time, a challenge that is further amplified for child speakers. Test-time adaptation (TTA) methods have shown great potential in bridging this domain gap. However, the use of TTA to adapt ASR models to the individual differences in each child's speech has not yet been systematically studied. In this work, we investigate the effectiveness of two widely used TTA methods-SUTA, SGEM-in adapting off-the-shelf ASR models and their fine-tuned versions for child speech recognition, with the goal of enabling continuous, unsupervised adaptation at test time. Our findings show that TTA significantly improves the performance of both off-the-shelf and fine-tuned ASR models, both on average and across individual child speakers, compared to unadapted baselines. However, while TTA helps adapt to individual variability, it may still be limited with non-linguistic child speech.