PERIT-STT · Healthcare

Appointment intake, triage lines and medication questions, dense with drug names and dosages.

Audio comes from front-desk intake, nurse lines, pharmacy callbacks. Scored on word error rate, with a working healthcare operator as the control.

1,020
Clips in the target held-out set
81h
Audio, never published
5
Languages in the set
1
Working healthcare operator as control

Leaderboard

Illustrative figures. Public runs begin when the held-out sets close.
ModelWord error rate (%)Entity accuracyDiarization error
Human control · sector transcriptionistHuman control
2.1 ± 2.2
99.1%3.4%
Frontier ASR · lab AFrontier
6.5 ± 1.9
93.4%10.4%
Specialist vendor ASRSpecialist
8.4 ± 1.0
88.7%9.2%
Frontier ASR · lab BFrontier
8.6 ± 0.7
90.3%10.3%
Frontier multimodal · lab CFrontier
10.1 ± 2.0
87.9%10.5%
Open weights · 8BOpen weights
17.6 ± 0.9
72.7%16.1%
Task families

What the set actually asks for.

  • Verbatim transcription
  • Entity capture (IDs, amounts, names)
  • Speaker diarization
  • Code-switching
  • Noisy & far-field audio
  • Domain vocabulary
Method & limits

How a run on this set is produced.

  1. 01Audio is held out and never published. A 60-clip development split per sector is open for calibration.
  2. 02Reference transcripts are written by two annotators independently; a senior reviewer adjudicates every disagreement.
  3. 03Each clip is run 3 times per model. Reported intervals are bootstrapped at 95%.
  4. 04Human control is a working transcriptionist from the same sector, timed and paid at market rate.

Limits, stated up front: 5 languages only, single-channel audio, and no cross-sector transfer. The control is one operator per sector, so the human line carries its own error bar — it is a working standard, not a ceiling.