How real-time transcription works
Audio is captured in small chunks, often tens of milliseconds long, and streamed to a speech recognition engine over a persistent connection such as a WebSocket. The engine emits partial hypotheses almost immediately, then finalizes each phrase once it has enough context. This is why live captions sometimes rewrite themselves: the model revises earlier words as the sentence completes.
In a meeting context, a bot inside the call forwards the live audio to the engine, and the resulting text is pushed to your application as a stream of timestamped, speaker-labeled segments while people are still talking.
Real-time vs asynchronous transcription
Asynchronous, or batch, transcription processes a complete recording after the meeting and can use full context, so it is usually slightly more accurate. Real-time transcription trades a little accuracy for immediacy, which is essential whenever the text must be used during the conversation rather than after it. Many products use both: a live stream during the call and a corrected async pass afterward for the permanent record.
What real-time transcription enables
Live text unlocks applications that a post-call transcript cannot serve:
- Live captions and accessibility for participants who are deaf or hard of hearing
- Real-time meeting agents that answer questions or fetch data mid-call
- Live sales coaching, objection detection, and battle cards
- Instant compliance monitoring for regulated conversations
- Running notes that are already drafted when the meeting ends
What to look for in a live transcription pipeline
Latency is the headline metric, both time to first partial result and time to final text. Beyond that, check speaker attribution on the live stream, punctuation and formatting quality, vocabulary customization for domain terms, language coverage, and whether the provider can also deliver a higher-accuracy async transcript of the same call. If you plan to build agents that respond during the meeting, measure the full loop from spoken word to model response, since transcription is only one leg of that journey.
Real-time transcription in practice
MeetStream bots stream meeting audio in roughly 200ms over WebSocket and deliver both real-time and async transcripts with speaker identification, so a single integration covers live agents and polished post-call notes across Zoom, Google Meet, and Microsoft Teams. See the MeetStream docs for the streaming reference. See the meeting bot API page to go deeper.
Frequently asked questions
How fast is real-time transcription?
Partial results typically appear within a few hundred milliseconds to a second of speech, with finalized text following shortly after. End-to-end latency depends on audio capture, network transport, and the recognition engine itself.
Is real-time transcription less accurate than batch?
Slightly, in most cases. Streaming models decode with limited future context, while batch models see the whole recording. The gap has narrowed, and many teams run a live stream for immediacy plus an async pass for the permanent record.
Do I need a meeting bot for real-time transcription?
For meetings on platforms like Zoom, Google Meet, or Microsoft Teams, something has to be in the call to hear it. A meeting bot joins as a participant and forwards live audio to the transcription engine without requiring installs from attendees.
Put the vocabulary to work
One API to join, record, stream, and transcribe meetings on Zoom, Google Meet, and Microsoft Teams.