How Accurate Is Automated Teams Call Transcription?

A transcript is only useful if it's accurate

Transcription turns a recorded call into text you can search, review, and audit in seconds instead of replaying audio. But an inaccurate transcript is worse than none — it creates a record people trust that quietly misstates what was said. So the real question isn't whether your Teams calls can be transcribed; it's what makes the transcript accurate enough to rely on. Here is what actually moves the needle.

Audio quality comes first

Speech recognition can only transcribe what it can clearly hear. Poor microphones, background noise, weak network connections that degrade audio, and heavy crosstalk all reduce accuracy before any model gets involved. The single biggest thing most organizations can do to improve transcripts is improve the audio going into them — good headsets, quiet environments, and stable connections.

Per-participant audio changes the game

This is where how you record matters as much as what transcribes it. When everyone is captured on a single mixed track, the transcription engine has to untangle overlapping voices from one signal — and attribution suffers when people talk over each other. Our recording bot captures per-participant (unmixed) audio, so each speaker is on their own track. That cleaner separation makes both the transcription and the speaker attribution more reliable. See how it's captured on the recording bot page.

Diarization: who said what

Accuracy isn't only about the words — it's about attributing them to the right person. That's speaker diarization: separating a conversation into segments by speaker. We use Azure AI Speech-to-Text with diarization, and because the audio is already separated per participant, the transcript can label speakers far more dependably than diarizing a single mixed file after the fact. Full detail is on the transcription page.

Domain vocabulary and accents

Every industry has terms a general model may not expect — drug names, ticker symbols, legal Latin, product SKUs — and speaker accents vary widely. These naturally challenge any speech engine. We won't quote a single accuracy percentage, because a real number depends on your audio, your speakers, and your vocabulary; anyone who promises one figure for every call is guessing. What we will say is that clean, separated audio gives you the best possible starting point.

Making transcripts audit-ready

For compliance, a transcript needs to be findable and defensible, not just readable. Because every transcript is stored alongside its recording in your own SharePoint library, you can search by keyword and speaker, tie the text back to the original audio, and apply retention — supporting audit, supervision, and eDiscovery. Read more on SharePoint storage and recording for legal teams.

The takeaway

Transcription accuracy is a chain: good audio, captured per-participant, transcribed with diarization, stored where you can verify it. Get the capture right and everything downstream gets easier. That's the approach behind our compliance recording service.