Short answer: Speaker diarization is the process of automatically separating an audio recording into segments by who is speaking — answering "who spoke when." In meeting notes, it's what turns a wall of text into a labeled transcript ("Sarah: …", "Marcus: …") so you can follow the conversation and attribute decisions to the right person.
What does speaker diarization mean?
Speaker diarization (sometimes written "diarisation") is a step in audio processing that detects how many distinct speakers are in a recording and marks which parts each one said. It works separately from transcription: transcription turns speech into text, while diarization decides which speaker each stretch of text belongs to. Together they produce a transcript where every line is attributed to a speaker.
Why does speaker diarization matter for meetings?
Without diarization, a meeting transcript is one undivided block of text — accurate, but hard to follow and impossible to attribute. With it, you can see who raised a concern, who committed to an action item, and who made a decision. That attribution is what makes a transcript useful as a record: you can search for what a specific person said, and action items can be tied to the right owner.
Generic labels vs. real names
Basic diarization produces generic labels — "Speaker 1", "Speaker 2". Turning those into real names requires extra signals: a library of enrolled voiceprints, the meeting's calendar attendee list, or a participant roster from the call platform. The more of these signals a tool has, the more reliably it can put real names on the transcript.
How CraftNote handles speaker diarization
CraftNote separates speakers automatically on any recording it captures, then attaches real names using your enrolled voiceprints and the meeting's calendar attendees. When its optional meeting bot joins a Zoom, Teams, or Google Meet call, it can also read the participant list for more reliable names. The result is a labeled, searchable transcript with an AI summary and action items. Try CraftNote or read how well it summarizes meetings.
Common Questions
What is the difference between transcription and diarization?
Transcription converts spoken audio into text. Diarization separates that audio by speaker, deciding who said which parts. A fully labeled transcript uses both: the words come from transcription, the speaker labels come from diarization.
How accurate is speaker diarization?
Accuracy depends on audio quality, how many speakers there are, and how much they overlap. Clear audio with distinct speakers diarizes well; heavy crosstalk and noisy rooms lower accuracy for any tool.
Can diarization identify real names automatically?
Generic labels like "Speaker 1" work on any recording. Real names require additional signals such as enrolled voiceprints, calendar attendees, or a platform participant roster.
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