# Comparing Human and Automatic Recognition of Dutch Dysarthric Continuous Speech: A Case Study

arXiv: https://arxiv.org/abs/2606.30237 · HTML: https://mlsanity.com/paper/2606.30237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 60 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [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)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2109.13226/) (2021)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [English Conversational Telephone Speech Recognition by Humans and Machines](https://mlsanity.com/paper/1703.02136/) (2017)
- [Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages](https://mlsanity.com/paper/2511.09690/) (2025)
- [On the limit of English conversational speech recognition](https://mlsanity.com/paper/2105.00982/) (2021)
- [Granary: Speech Recognition and Translation Dataset in 25 European Languages](https://mlsanity.com/paper/2505.13404/) (2025)
- [Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets](https://mlsanity.com/paper/2110.04612/) (2021)
- [Exploring data augmentation in bias mitigation against non-native-accented speech](https://mlsanity.com/paper/2312.15499/) (2023)
- [CDSD: Chinese Dysarthria Speech Database](https://mlsanity.com/paper/2310.15930/) (2023)
- [Good practices for evaluation of machine learning systems](https://mlsanity.com/paper/2412.03700/) (2024)
- [Toward Zero Oracle Word Error Rate on the Switchboard Benchmark](https://mlsanity.com/paper/2206.06192/) (2022)
- [Benchmarking Commercial Speech Recognition and Multimodal Large Language Models on Dysarthric Speech: Severity-Stratified Baselines and Architecture-Specific Prompting Effects](https://mlsanity.com/paper/2512.17474/) (2025)

## Abstract (arXiv)

In our goal to develop personalised dysarthric speech recognition (DSR) models, this study compared the recognition performances of human listeners and those of three state-of-the-art, off-the-shelf ASR systems (Whisper-large-V3, Google Chirp 3, and Omnilingual) on the recognition of Dutch continuous read and spontaneous speech from a single speaker with severe dysarthria. Results showed that both humans listeners and the three off-the-shelf ASR systems exhibit word error rates (WER) exceeding 70% on average, indicating that DSR is highly challenging for both humans and ASR systems. Fine-tuning on the dysarthric speech significantly reduced WER. Although overall WERs are still quite high (>23%), the personalised DSR models outperformed the human listeners, and performance is getting closer to being useful for supporting day-to-day communication of dysarthric speakers. Future research should focus on improving personalized DSR on spontaneous speech and longer utterances in the case of read speech, with a specific focus on particular phonemes.