How accurate does medical speech recognition need to be for Cantonese?
For clinical use, word-level accuracy below roughly 95% creates a correction experience frustrating enough to negate the time savings, so a medical scribe must reach and hold that threshold. Cantonese is one of the hardest tests for medical speech AI because clinicians code-switch between Cantonese, Mandarin and medical English mid-sentence, and because medical vocabulary rarely appears in consumer speech training data. Micromeet's AI Scribe (Voice-to-EMR) reaches 95%+ accuracy on Cantonese medical speech on our internal medical dataset.
Generic consumer speech recognition fails in Cantonese clinical settings for three reasons: medical terminology (drug names, dosing, lab abbreviations) is largely absent from consumer training data; clinicians code-switch between languages within a single utterance; and clinics are acoustically noisy. A clinical-grade system has to be purpose-built for these conditions, not adapted from a Western English-first product.
Why the ~95% threshold matters: below it, the doctor spends so long correcting the transcript that the scribe adds work instead of removing it. Above it, the clinician reviews rather than retypes — which is the whole point of a scribe. The right design also keeps a human in the loop: the AI drafts the structured note, and the clinician reviews and approves before anything enters the record.
This is Micromeet AI for clinical documentation: AI Scribe / Voice-to-EMR (V2N) supports 50+ languages including Cantonese, Mandarin, Indonesian and English, and runs as governed healthcare AI — AI writes, doctors decide.
Related questions
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Micromeet — AI for governed healthcare. MCU CoPilot, AI Scribe (Voice-to-EMR), AI Front Desk, Care Loop, Claim Readiness and AI Care Command Center — every output doctor-reviewed. AI writes. Doctors decide. See the public benchmark →