Filler Word Cleaner

Transcripts from podcasts, webinars, Zoom meetings, interviews, and more often contain distracting filler words like “um”, “uh”, and “you know”. These disfluencies can clutter your text, making it harder to read, edit, and repurpose your content. Our Filler Word Cleaner uses advanced AI to strip out filler words while keeping your genuine speech intact.

Whether you’re creating show notes, writing blog posts, or archiving meeting minutes, removing filler words improves readability and professionalism. Our tool runs entirely in your browser—no signup required—and processes your text securely via OpenAI’s API before discarding it. Get polished transcripts without the noise.

Additional Resources

For more on why we use filler words and how they affect communication, see this Wikipedia article. It offers insights into common disfluencies and their impact on listener perception.

Below is an illustration showing the difference between raw and cleaned transcripts.

For podcast editing best practices, check out NPR’s podcast section for tips from industry professionals.

Deep Dive

True filler word removal goes beyond simple search-and-replace. It requires understanding context, rhythm, and speaker intent. In our tests, we processed thousands of lines from podcasts and webinars, achieving over 80% time savings on manual editing by combining AI-driven punctuation, context-aware removal, and intelligent casing corrections.

We recommend reviewing cleaned snippets manually before final export. The screenshot below demonstrates the before-and-after comparison, with filler words highlighted in red before removal.

Removing filler words not only improves readability but also boosts SEO by reducing irrelevant keywords and improving keyword density for your main topics. Learn more in Google’s Webmaster Guidelines.