Parakeet-TDT 0.6B HD Articulation
Official checkpoint for the paper "Huntington Disease Automatic Speech Recognition with Biomarker Supervision."
Model description
This model is an articulation-aware variant of Parakeet-TDT 0.6B HD, tuned for automatic speech recognition on speech affected by Huntington disease (HD). It extends the HD-adapted base model with auxiliary supervision from articulatory biomarker labels.
What this model does
The model transcribes English read / controlled clinical speech from speakers with Huntington disease and healthy controls. It is intended as a research model for studying robust ASR under hyperkinetic motor-speech disruption and for analyzing the effect of articulatory supervision on transcription behavior.
Training
The model was initialized from charleslwang/parakeet-tdt-0.6b-HD and further adapted using parameter-efficient encoder-side adapters with an auxiliary objective based on articulatory biomarker labels, while keeping the pretrained backbone frozen.
Evaluation
On the reported HD test set, this model achieved:
- WER: 6.44
- Substitutions: 1.94
- Deletions: 3.21
- Insertions: 1.29
Intended use
This model is intended for:
- research on pathological / atypical speech recognition,
- benchmarking ASR on Huntington disease speech,
- studying how articulatory auxiliary supervision reshapes error behavior.
It is not intended for clinical diagnosis, treatment decisions, or standalone medical use.
Limitations
- Trained and evaluated on a relatively small, high-fidelity clinical corpus.
- Primarily reflects controlled / read speech rather than spontaneous conversational speech.
- Did not outperform the plain HD-adapted model on overall WER.
- May not generalize to severe out-of-distribution impairment, other languages, or other recording conditions.
Citation
If you use this model, please cite:
1@article{wang2026huntington,
2 title={Huntington Disease Automatic Speech Recognition with Biomarker Supervision},
3 author={Wang, Charles L. and Chen, Cady and Gong, Ziwei and Hirschberg, Julia},
4 journal={arXiv preprint arXiv:2603.11168},
5 year={2026},
6 url={https://arxiv.org/abs/2603.11168}
7}
