MedWER: A Reproducible, Model-Free Evaluation Protocol for Medical Speech Recognition
Overall word error rate hides clinically critical errors: a transcript can be 95% correct and still swap one drug for another. The usual fix weights errors on medical entities, and almost always depends on an evaluation-time named-entity recognition (NER) model or cloud API, which makes the metric's denominator a versioned black box. We present MedWER, an evaluation protocol and open-source tool for medical ASR whose denominator is a fixed, license-clean term list: 19,373 drug, diagnosis, symptom, and injury-mechanism entries projected from public sources. The protocol couples a pinned text normalizer with a phrase-aware term-restricted WER, the MedWER, so the only versioned component is ...
| Authors | Justin Behling |
|---|---|
| Primary category | cs.CL |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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