Microsoft launches real-time transcription. How do you tell whether meeting notes are useful?
Check real-time output and speaker-labeling limits in MAI-Transcribe-2-Streaming, learn how interim text is revised, and use the meeting test design and evaluation table. The model was not called for this guide.
What to consider
MAI-Transcribe-2-Streaming supports 60 languages and automatic language detection. It receives audio continuously, revises interim text as context arrives, and then commits final text. For meetings, first establish whether the record needs speaker labels. Microsoft’s comparison table, checked on October 20, 2026, lists no speaker diarization or word-level timestamps for the streaming version. Azure integration remains in public preview. This guide provides a test design, not results from calling the model.
Live captions and post-meeting notes need different features
Live text helps participants follow speech, but a meeting record often also needs to identify who spoke. Microsoft lists MAI-Transcribe-2 separately from MAI-Transcribe-2-Streaming: the former includes speaker diarization and word-level timestamps, while the streaming version currently does not. Do not apply the feature summary for the whole product family to the streaming version, especially for meetings with several speakers.
For projected captions, begin by testing how stable the text is. To produce owners and action items automatically, add a separate way to match statements to speakers or ask participants to confirm them. The table summarizes documented features as of the check date; it does not compare recognition accuracy. Check availability in your Azure region before integration rather than treating preview documentation as an already configured service.
| Meeting feature | MAI-Transcribe-2 | MAI-Transcribe-2-Streaming |
|---|---|---|
| Real-time streaming | Not provided | Provided |
| Speaker diarization | Provided | Not provided |
| Word-level timestamps | Provided | Not provided |
| Keyword biasing | Provided | Not provided |
Keep interim text separate from final text
The streaming model card distinguishes fast interim hypotheses produced during speech from stable final results after an utterance. Interim text can change, so a caption interface needs to replace the current segment. Appending every intermediate update to the notes can leave duplicates and superseded errors. Make it explicit which output a downstream summarizer uses.
Record a dedicated two-minute sample with product names and dates, followed by a correction such as “Submit on Wednesday—change that to Thursday.” Keep the audio and a record of caption changes, then check whether the final text retains the corrected date. Two minutes is a convenient length for manual playback, not a Microsoft audio-duration limit or a setting established by our own tests.
Use known answers to check that meeting decisions survive
Prepare a multi-speaker sample with an explicit outcome, and manually record task owners and the final deadline. Include the language switching your team normally uses instead of selecting only clear speech with no interruptions. Check the transcript sentence by sentence before checking its action-item summary. Count speech-recognition errors separately from summarization errors so you can identify the step that needs work.
Measure the time from starting the process to participant confirmation of usable notes, including corrections and playback. If a negation is missing or an open question becomes a supposed decision, keep the corresponding audio location for review. The table below is a test template with no preset accuracy or timing results. Calculate those figures from the actual inputs and outputs rather than copying a public leaderboard.
| Test segment | Manually established answer | Result to record |
|---|---|---|
| A spoken date correction | Use the last confirmed date | Does the final text retain the old date? |
| Two speakers taking different tasks | Match each task to its actual owner | Locate transcript and summary errors separately |
| A discussion with no decision | Keep it as an open item | Was it turned into a committed action? |
Before integration, complete one test you can replay
Microsoft Learn lists two integration options: an OpenAI Realtime-compatible WebSocket API and the Azure Speech SDK. Teams already using either client can begin with the matching official guide. First obtain interim and final text from one recording, then add summarization. The service is in public preview, has no service-level agreement, and is not recommended by Microsoft for production workloads.
We checked the official model card, version comparison, and Azure integration overview. We did not create paid resources or upload meeting audio. The sample design can support a later test but does not establish suitability for a particular team. After testing, retain the audio, final transcript, and participant-confirmed notes. When publishing results, specify the language and recording conditions instead of turning a small sample into a general accuracy claim.
Checked sources
The following sources support the project descriptions and reported facts. The analysis is by the site editors; claims from these sources are not presented as results from our own tests.