# Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR

arXiv: https://arxiv.org/abs/2305.00115 · HTML: https://mlsanity.com/paper/2305.00115/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2023.

## Built on by (most cited first, 23 of 23)

- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [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)
- [Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet](https://mlsanity.com/paper/2508.16576/) (2025)
- [Exploration of Adapter for Noise Robust Automatic Speech Recognition](https://mlsanity.com/paper/2402.18275/) (2024)
- [Children's Speech Recognition through Discrete Token Enhancement](https://mlsanity.com/paper/2406.13431/) (2024)
- [TICL+: A Case Study On Speech In-Context Learning for Children's Speech Recognition](https://mlsanity.com/paper/2512.18263/) (2025)
- [A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognition](https://mlsanity.com/paper/2311.04936/) (2023)
- [Employing self-supervised learning models for cross-linguistic child speech maturity classification](https://mlsanity.com/paper/2506.08999/) (2025)
- [Can large audio language models understand child stuttering speech? speech summarization, and source separation](https://mlsanity.com/paper/2510.20850/) (2025)
- [UniEnc-CASSNAT: An Encoder-only Non-autoregressive ASR for Speech SSL Models](https://mlsanity.com/paper/2402.08898/) (2024)
- [Evaluation of Speech Foundation Models for ASR on Child-Adult Conversations in Autism Diagnostic Sessions](https://mlsanity.com/paper/2409.16135/) (2024)
- [SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research](https://mlsanity.com/paper/2506.09206/) (2025)
- [K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function](https://mlsanity.com/paper/2507.03043/) (2025)
- [Can Layer-wise SSL Features Improve Zero-Shot ASR Performance for Children's Speech?](https://mlsanity.com/paper/2508.21225/) (2025)
- [Entropy-Aware Domain-Routed Mixture-of-Experts Speech-LLM Framework: A Case Study of Multi-Domain Child-Adult ASR](https://mlsanity.com/paper/2606.10454/) (2026)
- [Using State-of-the-Art Speech Models to Evaluate Oral Reading Fluency in Ghana](https://mlsanity.com/paper/2310.17606/) (2023)
- [Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search](https://mlsanity.com/paper/2310.17664/) (2023)
- [Causal Structure Discovery for Error Diagnostics of Children's ASR](https://mlsanity.com/paper/2506.00402/) (2025)
- [Audio-3DVG: Unified Audio -- Point Cloud Fusion for 3D Visual Grounding](https://mlsanity.com/paper/2507.00669/) (2025)
- [Abjad-Kids: An Arabic Speech Classification Dataset for Primary Education](https://mlsanity.com/paper/2603.20255/) (2026)
- [Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy](https://mlsanity.com/paper/2604.01705/) (2026)
- [Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR](https://mlsanity.com/paper/2606.19791/) (2026)

## Abstract (arXiv)

Recently, self-supervised learning (SSL) from unlabelled speech data has gained increased attention in the automatic speech recognition (ASR) community. Typical SSL methods include autoregressive predictive coding (APC), Wav2vec2.0, and hidden unit BERT (HuBERT). However, SSL models are biased to the pretraining data. When SSL models are finetuned with data from another domain, domain shifting occurs and might cause limited knowledge transfer for downstream tasks. In this paper, we propose a novel framework, domain responsible adaptation and finetuning (DRAFT), to reduce domain shifting in pretrained speech models, and evaluate it for a causal and non-causal transformer. For the causal transformer, an extension of APC (E-APC) is proposed to learn richer information from unlabelled data by using multiple temporally-shifted sequences to perform prediction. For the non-causal transformer, various solutions for using the bidirectional APC (Bi-APC) are investigated. In addition, the DRAFT framework is examined for Wav2vec2.0 and HuBERT methods, which use non-causal transformers as the backbone. The experiments are conducted on child ASR (using the OGI and MyST databases) using SSL models trained with unlabelled adult speech data from Librispeech. The relative WER improvements of up to 19.7% on the two child tasks are observed when compared to the pretrained models without adaptation. With the proposed methods (E-APC and DRAFT), the relative WER improvements are even larger (30% and 19% on the OGI and MyST data, respectively) when compared to the models without using pretraining methods.