Hugging Face's logo

charleslwang
/
parakeet-tdt-0.6b-HD-articulation

Automatic Speech Recognition
NeMo
English
speech
pathological-speech
dysarthria
huntingtons-disease
parakeet
multitask-learning
articulation
Eval Results (legacy)
Model card Files
xet
 Community
parakeet-tdt-0.6b-HD-articulation
File size: 2,867 Bytes
c471e55
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
959b8f3
c471e55
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bf627f4
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
---
language:
- en
tags:
- automatic-speech-recognition
- speech
- pathological-speech
- dysarthria
- huntingtons-disease
- nemo
- parakeet
- multitask-learning
- articulation
license: apache-2.0
pipeline_tag: automatic-speech-recognition
library_name: nemo
base_model: charleslwang/parakeet-tdt-0.6b-HD
model-index:
- name: parakeet-tdt-0.6b-HD-articulation
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Huntington Disease clinical speech test set
      type: private
    metrics:
    - type: wer
      value: 6.44
      name: WER (%)
---

# 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:

```bibtex
@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}
}