Speaker Diarization: What It Is and How the Tools Compare
Who spoke when, and how far to trust the claims.
Speaker diarization splits a recording by who spoke when, and labels each part Speaker 1, Speaker 2 and so on. No one independent test ranks today's tools, and vendors quote results from their own tests. The fair way to choose is to run the same recording of your own through each tool.
Checked November 2026 on each tool's own site.
How each tool labels speakers
| Tool | How it works | Worth knowing |
|---|---|---|
| AssemblyAI | An API setting labels the speakers in a file | Its docs advise at least 30 seconds of speech per speaker |
| Deepgram | An API setting that also picks the model version | The v2 model works on files, not on live streams |
| pyannoteAI | A paid API, plus the free open-source pyannote.audio library | It claims up to 52% lower error, and the API starts at €19 a month |
| Otter | Live names come from the meeting platform, and tagged voices are recognized later | A new voice stays Speaker # until you tag it |
| Fireflies | Its bot labels speakers, and picks up names in online meetings | Recording without the bot shows only Speaker 1 and Speaker 2 |
Which speaker diarization tool is the most accurate?
No one can say for your audio without testing it. pyannoteAI calls its diarization the most accurate in the field, but other vendors make similar claims from their own tests. Results change with the number of speakers, crosstalk and microphones.
How is speaker diarization accuracy measured?
The usual measure is the diarization error rate, or DER. It is the share of the recording's time given to the wrong speaker, missed, or marked as speech when no one spoke. A lower number is better.
What is the difference between diarization and speaker identification?
Diarization only separates the voices. Speaker identification puts a name on each one, using a voice it knows or the names from the meeting platform.
How can I get better speaker labels?
Give each person their own microphone and avoid talking over each other. If your API allows it, set the number of speakers, but only when you know it, because AssemblyAI warns that a wrong count can hurt accuracy.