Speaker diarization
Speaker diarization: how software knows who spoke when
Speaker diarization is the process of splitting a recording by speaker. In simple terms, transcription answers “what was said?” while diarization answers “who spoke when?”
Transcription and diarization solve different problems
Speech-to-text turns sound into words. Diarization groups those words into Speaker 1, Speaker 2 and so on. Combining both turns a long transcript into something that reads like a conversation.
Speaker recognition goes one step further
Diarization can tell that two voices are different without knowing their names. Speaker recognition links a voice to a known person, such as recognizing that Speaker 2 is Alice because you named her in an earlier recording.
Why it matters for meetings and interviews
When decisions, quotes and action items depend on who said them, a plain transcript is incomplete. Speaker labels make it faster to verify statements, review interviews and follow a multi-person discussion.
Diarization is not perfect
Overlapping speech, distant microphones, background noise and very similar voices can cause mistakes. A good app should let you rename, merge and reassign speakers instead of treating the first result as final.
