parakeet-tdt-0.6b-HD-articulation

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:

@article{wang2026huntington,
  title={Huntington Disease Automatic Speech Recognition with Biomarker Supervision},
  author={Wang, Charles L. and Chen, Cady and Gong, Ziwei and Hirschberg, Julia},
  journal={arXiv preprint arXiv:2603.11168},
  year={2026},
  url={https://arxiv.org/abs/2603.11168}
}