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AfriHealth AI reports speech recognition accuracy from a 15-case Amharic-English clinical benchmark. This page summarizes the measured results, explains which views are demo fixtures, and shows how to run your own accuracy checks in the Evidence Review section of the prototype.

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Measured results

Normalized scores on saved Amharic-English transcript outputs (15 verified references): A separate 15-recording clinical review found 56.38% mean WER, 44.33% target-term recall, and critical-term misses in 6 cases.
Offline scoring uses stored hypotheses and does not rerun model inference. Wav2Vec2 is an English-only baseline. Target recall is not a fairness metric. These results are not population-performance evidence, and clinician review is mandatory.

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Fixture benchmark matrix

The 4-Model Benchmark Matrix for the five Gold Standard cases is a demo view. Its WER/CER values are illustrative fixture examples, not a real model ranking. Use the measured results above for performance claims.

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Run your own checks

1

Live benchmark

In Live Intron Sahara v2.5 Benchmark, upload or record a sample, optionally pick a Gold Standard case (CS-01 to CS-15), and select Run Live Benchmark.
2

Edit-distance calculator

In Live Levenshtein Edit-Distance Calculator, paste a reference transcript and a model hypothesis, then select Compute Exact WER & CER.
The calculator uses WER = (S + D + I) / N, where S, D, and I are substitutions, deletions, and insertions, and N is the number of reference words.