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Speaker-Attributed Transcription Toward Automated Scoring of NACC Uniform Data Set Assessments: A Whisper–Pyannote Pipeline and Pilot Evaluation
Ibtisum S, Qader MA.. Preprints.org, 2026-10-12
Topic: Brain imaging, connectivity and assessment
Standardized cognitive and functional assessments, such as those in the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS), are central to detecting and monitoring Alzheimer’s disease, yet their administration and scoring depend on trained examiners who record and score responses by hand. We present a modular speech-processing pipeline that captures and structures spoken responses from UDS assessments administered by a clinician or by the Zeus2Q humanoid robot. The pipeline converts recordings to 16-kHz mono audio, segments speakers with the pyannote.audio 3.1 diarization pipeline, transcribes each speaker turn with Whisper medium.en, optionally personalized by participant-level fine-tuning, and emits a speaker-attributed, time-stamped JSON record aligned with UDS items for downstream scoring. The pipeline runs end to end on Google Colab and on an NVIDIA H200 GPU cluster. In a pilot on volunteer recordings of UDS-4 Form B7, the NACC Functional Assessment Scale, a fine-tuned Whisper model achieved a normalized word error rate (WER) of 25.7% and a character error rate of 18.1% on an evaluation recording. Error analysis showed that a single deleted response would produce at least two incorrect item codes if responses were assigned to items in order of occurrence, so WER alone does not capture clinical utility. Continued training and a single-example run drove the training loss to near zero within 40 steps, which reflects memorization rather than generalization and highlights the overfitting risk of few-sample personalization. We specify the evaluation protocol for a planned 200-participant study: diarization error rate, held-out WER against a zero-shot baseline, and item-level agreement with clinician scoring.
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APA 7th
Ibtisum, S., & Qader, M. A. (2026). Speaker-Attributed Transcription Toward Automated Scoring of NACC Uniform Data Set Assessments: A Whisper–Pyannote Pipeline and Pilot Evaluation. Preprints.org. https://doi.org/10.20944/preprints202610.1095.v1
Vancouver
Ibtisum S, Qader MA. Speaker-Attributed Transcription Toward Automated Scoring of NACC Uniform Data Set Assessments: A Whisper–Pyannote Pipeline and Pilot Evaluation. Preprints.org. 2026. doi:10.20944/preprints202610.1095.v1.
BibTeX
@article{ibtisum2026-5259a2b8,
author = {Ibtisum, S. and Qader, M. A.},
title = {{Speaker-Attributed Transcription Toward Automated Scoring of NACC Uniform Data Set Assessments: A Whisper–Pyannote Pipeline and Pilot Evaluation}},
journal = {Preprints.org},
year = {2026},
doi = {10.20944/preprints202610.1095.v1},
url = {https://doi.org/10.20944/preprints202610.1095.v1}
}
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Key findings
- Error analysis showed that a single deleted response would produce at least two incorrect item codes if responses were assigned to items in order of occurrence, so WER alone does not capture clinical utility.
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Study details
- Effect size25.7%; 18.1%
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 24 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 15 of the 62 points we could judge it on, which is 24 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Preprints.org) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 15 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 15 of the 62 points we could judge it on is 24 out of 100. |
| Total | 24 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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