Otter.ai speaker identification best practices help ensure speakers are accurately labeled in meeting transcripts and recordings. Using clear audio, participant labeling, and reviewing speaker labels in Otter improves transcription accuracy, searchability, and AI-generated meeting summaries.
Speaker Identification Best Practices
1. Consistent Naming Across Systems
Ensure that each participant’s display name is consistent across:
- Calendar (used to schedule meetings)
- Video conferencing system (e.g., Zoom, Google Meet, Microsoft Teams)
- Otter.ai account
Why this matters: Consistent names allow Otter to match speaker enrollments more accurately and speed up labeling.
Example of inconsistent naming:
| System | Name | Recommended |
|---|---|---|
| Microsoft Teams | John (ACME Solutions) | John Smith |
| Calendar | Johnny A. Smith | John Smith |
| Otter.ai | Johnathan Smith | John Smith |
2. Optimize Audio Quality
Good audio directly improves speaker recognition accuracy.
-
Get close to the microphone
- Keep the mic close and speak clearly.
- Just as humans hear better when nearby, Otter performs better with clear, close-up audio.
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Reduce ambient noise
- Turn off fans, background chatter, and echo.
- Be careful with noise-canceling mics, as they may clip or distort voices. Learn more about troubleshooting audio problems.
- Avoid overlapping speech
- Avoid speaking over one another. When multiple people talk simultaneously, it becomes less clear to whom Otter should correctly attribute a speaker.
- Consider pausing briefly or allowing each speaker to finish their sentence to maintain clean, distinguishable audio.
3. Microphone Usage
- Minimize mic sharing: if multiple people speak into the same mic, Otter can struggle to separate speakers when voice quality is poor in a large room. Refer to the Optimize Audio Quality section for more info.
- Use individual headsets or mics when possible, or ensure multiple microphones in a large room setting.
Tip: Use an omnidirectional microphone when recording multiple people in a room to capture sound from all directions. Use a unidirectional microphone when recording yourself in an online meeting for clearer audio.
5. Tagging and Corrections
- Tag speakers when Otter does not label them, such as when they appear as 'Speaker 1', 'Speaker 2', or 'Unknown Speaker'. Learn more about tagging speakers.
- Correct inaccurate speaker labels and ensure each paragraph contains audio from only one speaker. Mixing multiple speakers in a single paragraph can reduce future speaker identification accuracy. Learn more about updating speaker tags.
- Doing so trains the system and improves future SID accuracy. Learn more about speaker identification.
6. Pro Tip: Label speakers proactively
- Use Otter’s Import feature to upload audio files from past conversations containing speakers you speak with the most.
- Once the file has finished processing, open the transcript and label each speaker. This helps improve speaker tagging and increases speaker identification accuracy over time.
- By identifying speakers in advance, Otter gains useful context about voice patterns and vocabulary, resulting in more accurate future transcripts.
Summary
| Best Practice | Why It Matters |
| Speaker enrollment | Builds a voice profile for accuracy |
| Consistent naming | Enables system-level matching |
| Good mic proximity | Ensures clean audio capture |
| Noise reduction | Prevents loss of voice data |
| Limited mic sharing | Avoids confusion between speakers |
| Corrective tagging | Trains Otter to improve future SIDs |
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