# Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN Corpus

arXiv: https://arxiv.org/abs/2502.17284 · HTML: https://mlsanity.com/paper/2502.17284/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning](https://mlsanity.com/paper/2110.00678/) (2021)
- [Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decoding](https://mlsanity.com/paper/2202.05209/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data](https://mlsanity.com/paper/2506.11130/) (2025)
- [Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Older Adults](https://mlsanity.com/paper/2508.08684/) (2025)
- [Utterance-Level Methods for Identifying Reliable ASR-Output for Child Speech](https://mlsanity.com/paper/2604.19801/) (2026)
- [Transcribing Children's Speech: ASR Performance and Obtaining Reliable Orthographic Transcriptions](https://mlsanity.com/paper/2605.28833/) (2026)
- [Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs](https://mlsanity.com/paper/2609.38106/) (2026)

## Abstract (arXiv)

We test and study the variation in speech recognition of fine-tuned versions of the Whisper model on child, elderly and non-native Dutch speech from the JASMIN-CGN corpus. Our primary goal is to evaluate how speakers' age and linguistic background influence Whisper's performance. Whisper achieves varying Word Error Rates (WER) when fine-tuned on subpopulations of specific ages and linguistic backgrounds. Fine-tuned performance is remarkably better than zero-shot performance, achieving a relative reduction in WER of 81% for native children, 72% for non-native children, 67% for non-native adults, and 65% for native elderly people. Our findings underscore the importance of training speech recognition models like Whisper on underrepresented subpopulations such as children, the elderly, and non-native speakers.