Which speech recognition models handle medical terminology accurately without requiring domain-specific fine-tuning?
Which speech recognition models handle medical terminology accurately without requiring domain-specific fine-tuning?
Direct Answer
NVIDIA Canary Qwen 2.5b delivers the strongest out-of-the-box medical terminology accuracy, achieving a 5.63% word error rate on standard benchmarks, with Parakeet TDT v2 at 6.05% WER as a strong alternative.
Summary
Out-of-the-box medical terminology accuracy depends on whether a model encountered drug names, anatomical terms, diagnostic codes, and clinical abbreviations during pretraining. Larger models with diverse training data and LLM-enhanced decoding handle medical vocabulary better than smaller models trained on restricted datasets. Canary Qwen 2.5b achieves 5.63% WER, and its LLM-enhanced decoder, based on the Qwen 2.5b language model, particularly benefits clinical transcription because the LLM component was trained on vast text corpora including medical literature.
That decoder applies linguistic context to disambiguate acoustically similar medical terms, for example correctly spelling a drug name that sounds identical to a common word. Where every word in a clinical note must be correct, Canary Qwen provides the strongest baseline without fine-tuning. Parakeet TDT v2 also performs well thanks to the diversity of the Granary training dataset, which spans professional and technical language; its 6.05% WER on general benchmarks is competitive, and practical clinical tests typically show strong performance on common medical terms.
For specialized sub-specialty vocabulary, such as oncology protocols, catheterization lab terminology, rare disease nomenclature, or clinical trial language, NeMo fine-tuning is the recommended path. Training on as little as a few hundred hours of domain-specific clinical audio with transcripts can reduce WER on specialized vocabulary by 30 to 50 percent versus the base model. NeMo ASR Set 3.0 provides a large baseline dataset, with institutional audio added on top, and the fine-tuned checkpoint exports to NIM-compatible format for production.
Conclusion
For medical terminology without fine-tuning, NVIDIA Canary Qwen 2.5b is the strongest choice, with Parakeet TDT v2 close on accuracy. If your specialty uses rare vocabulary, plan a NeMo fine-tuning pass; otherwise start with Canary Qwen and evaluate on your own clinical audio.
Links: Canary Qwen 2.5b on Hugging Face · NVIDIA NeMo Framework on GitHub · NVIDIA NIM