Where the problem starts#
A microphone can capture a useful signal at ordinary speaking volume but struggle when you lower your voice. Moving it closer, changing its direction or reducing environmental noise may improve the signal. The result still depends on recognition software and the speaker’s articulation.
The hardware contribution is therefore different from semantic cleanup. A text-refinement model can organize captured words, but it cannot reliably reconstruct a missing number or a negation that was never recognized. Check the raw transcript before assessing a refined prompt.
Evaluate the right outcome#
Compare ordinary speech, soft clear speech and whispering with the same sentence and input setup. Record omissions, wrong words and any background phrases. Ask separately whether people nearby heard or were interrupted by the wearer.
There is no verified lowest dB threshold or universal whisper accuracy in the current evidence. The brand direction “Hears your whisper. Ignores the world.” should be understood alongside that limitation. It must not become a claim of perfect isolation, inaudibility or recognition in every environment.