Speaker diarization
Speaker diarization is the process of splitting an audio or video recording into segments according to who is speaking, answering the question "who spoke when?". It labels each segment with a speaker tag such as Speaker 1 or Speaker 2, without necessarily knowing their names. Combined with a transcript, diarization turns a block of text into a readable conversation.
Why it matters
A transcript without speaker labels is hard to read and easy to misquote. Diarization makes meeting notes, interviews and podcasts usable, enables per-speaker search and analytics, and supports correct attribution, which matters for journalism and sales coaching.
How it works
- The audio is split into short segments, and silence is removed.
- A model turns each segment into a voice fingerprint, a numeric summary of how the speaker sounds.
- Segments with similar fingerprints are clustered into the same speaker.
- Speaker labels are merged with the transcript so each line shows who said it.
What is the difference between speaker diarization and speaker recognition?
Diarization answers "who spoke when?" by grouping speech into anonymous speakers like Speaker 1 and Speaker 2. Speaker recognition answers "who is this?" by matching a voice to a known identity. Diarization needs no prior voice samples, so it works on any recording. Names can be added afterward by a person or by matching enrolled voices.
Why does speaker diarization get speakers wrong?
Errors usually come from people talking over each other, similar voices, very short turns and changing audio conditions, such as someone moving away from the microphone. The system may merge two people into one label or split one person into two. Separate microphones per speaker and telling the system how many speakers to expect usually improve results.
Where it's used
Meeting and sales-call software relies on speaker diarization to show who said what. News teams use it to attribute quotes correctly, and creator platforms produce clearer captions for podcasts and panels.
