Filler Word Removal for Interview Recordings
Picture this: You've recorded a brilliant interview. The insights are valuable. But every other sentence includes "um," "uh," or "you know."
Manual cleanup would take hours. Automated detection takes minutes.
Why Interviews Have More Fillers
Interviews create conditions for verbal fillers:
- Guests think on their feet
- Unexpected questions require processing time
- Nervous energy increases verbal tics
- Natural conversation includes more hesitation than scripted content
A typical 30-minute interview contains 80-150 filler instances.
What Counts as a Filler Word
Universal fillers:
- Um, uh, er, ah
- Like (non-comparative use)
- You know, I mean
- So (sentence starter)
- Basically, actually, literally
Context-dependent:
- "Right" as a verbal tic
- Repeated phrases ("the thing is, the thing is")
- Extended "and" or "but" at sentence starts
Detection Accuracy
Modern AI filler detection achieves:
- 85-92% accuracy on common fillers (um, uh)
- 75-85% accuracy on context-dependent fillers (like, you know)
- Lower accuracy on unusual speech patterns
False positives (legitimate words flagged as fillers) occur 3-5% of the time.
When to Remove vs. Keep
Remove when:
- Fillers interrupt the point being made
- High frequency makes listening difficult
- Professional polish is required
Keep when:
- Filler serves as thinking pause
- Removal would sound unnatural
- Speaker authenticity matters more than polish
Most interviews benefit from removing 60-80% of fillers while preserving natural speech rhythm.
Implementation
Automated filler detection flags each instance. Review mode lets you approve, reject, or modify each suggestion before the edit is applied.
Tools like Rendezvous handle filler word removal as part of generating a clean narrative cut from raw interview footage.
Clean up your interview recordings →
Content reviewed January 2026.