A hospital wants a hands-free clinical note taker: as a doctor dictates observations into a microphone during a ward round, the application must convert the spoken words into written text that is saved to the patient record in real time. Which capability in Azure Speech in Foundry Tools meets this requirement?
- AText-to-speech, which produces natural-sounding spoken audio from written input
- BSpeech-to-text, which transcribes the spoken audio into written text as it is captured Correct
- CSpeech translation, which converts spoken audio in one language into another language
- DText analysis, which extracts sentiment and key phrases from a body of written text
Why A is wrong: Text-to-speech runs in the wrong direction: it turns written text into audio, whereas the doctor is producing speech that must become text. It is tempting because both features live in Azure Speech, but it solves the reverse problem.
Why B is correct: The requirement is to turn dictated audio into written notes as they are spoken, which is exactly what speech-to-text does, supporting real-time transcription of a live microphone stream.
Why C is wrong: Speech translation is tempting because it also processes spoken input, but the scenario keeps a single language and only needs a transcript, not a change of language.
Why D is wrong: Text analysis operates on text that already exists and derives insights from it. It cannot capture spoken audio at all, so it fails the core dictation-to-text need despite sounding clinically relevant.