Accuracy by Meeting Type
Best-Case Performance
- 90-95% accuracy with 2-4 speakers
- Works best with clear turn-taking
- Benefits from preconfigured speaker profiles
Source [1]
Challenging Conditions
- 70-85% accuracy with 6+ speakers
- Similar-sounding voices reduce reliability
- Overlapping speech causes frequent attribution errors
Source [1]
How to Set Up Speaker Profiles
- Ask each recurring participant to record a 30-60 second voice sample.
- In Otter, go to Settings -> Speakers -> Add New Speaker.
- Upload each sample and assign clear speaker names.
- Retrain monthly to maintain recognition quality.
Source [1]
Limitations You Should Expect
- Similar voices can be confused, especially in larger groups.
- Overlapping speech lowers attribution quality.
- Speaker profiles work best when configured before meetings.
- Otter has a practical 10-speaker cap for identification.
Source [1]
Platform and Competitor Comparison
Platform Results
- Zoom: strongest performance via direct integration and participant mapping.
- Google Meet: generally good with extension-based participant access.
- Microsoft Teams: more manual relabeling is often needed post-meeting.
Source [1]
Otter vs Competitors
Compared with some alternatives such as Fireflies, Otter is less strong for multilingual diarization use cases.[1]
Sources
- Otter Speaker Identification: A Complete Guide to Accuracy and Setup (provided source article).
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