Question

You are building a voice-enabled agent for a hospital's radiology department. During testing, the standard Azure AI Speech-to-text model frequently mis-transcribes specialized medical terms (e.g., 'oligodendroglioma', 'cholecystectomy') that clinicians dictate, even though general conversational speech transcribes accurately. You have a corpus of correctly spelled domain terms and representative sentences but only a small amount of matching audio. Which approach best improves recognition accuracy for these terms?

Accepted Answer

Train a custom speech model in Speech Studio using plain-text and structured-text (pronunciation) training data built from the domain corpus, then deploy it to a custom endpoint the agent calls.

Answer

Switch the audio format to a higher sample rate and increase the microphone gain to capture clearer input before sending it to the standard model.

Answer

Add a post-processing LLM prompt that rewrites the standard transcription output, instructing it to guess and correct any misspelled medical words.

Answer

Enable text-to-speech neural voice output so the agent reads the terms back to confirm them with the clinician.