mlsanityData to 7 Oct 2026

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

arXiv 2606.30237 ↗First posted 29 Jun 2026cs.CLcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 60 references that are in the map. First posted on arXiv 29 Jun 2026.

Builds on

19 of 60 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  4. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  5. 2023Scaling Speech Technology to 1,000+ Languagescited by 401
  6. 2023Google USM: Scaling Automatic Speech Recognition Beyond 100 Languagescited by 228
  7. 2021BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognitioncited by 88
  8. 2016Achieving Human Parity in Conversational Speech Recognitioncited by 63
  9. 2019Personalizing ASR for Dysarthric and Accented Speech with Limited Datacited by 45
  10. 2017English Conversational Telephone Speech Recognition by Humans and Machinescited by 40
  11. 2025Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languagescited by 32
  12. 2021On the limit of English conversational speech recognitioncited by 30
7 more
  1. 2025Granary: Speech Recognition and Translation Dataset in 25 European Languagescited by 21
  2. 2021Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasetscited by 14
  3. 2023Exploring data augmentation in bias mitigation against non-native-accented speechcited by 11
  4. 2023CDSD: Chinese Dysarthria Speech Databasecited by 7
  5. 2022Toward Zero Oracle Word Error Rate on the Switchboard Benchmarkcited by 4
  6. 2024Good practices for evaluation of machine learning systemscited by 4
  7. 2025Benchmarking Commercial Speech Recognition and Multimodal Large Language Models on Dysarthric Speech: Severity-Stratified Baselines and Architecture-Specific Prompting Effectscited by 3

Built on by

Not cited yet.