What this does
Finds hesitations, stutters and immediately repeated phrases in your speech and cuts them on word boundaries, so the delivery tightens without the edit becoming audible.
Mechanical hesitations — um, uh, er — carry no meaning and can always go. Discourse words like 'like', 'you know' and 'I mean' sometimes do real work in a sentence, and removing all of them can change how you sound. If you use one deliberately, say so and it will be left alone.
How to do it
Follow these step-by-step instructions in Cubix.
Open a project with spoken audio
Filler removal reads what was said, so it works on any clip with speech in it.
Ask for the cleanup
Filler removal runs alongside silence removal in the same pass, so one request covers both. That is usually what you want — pauses and hesitations are the same problem.
Review what it caught
Cubix reports what it removed. Listen back once: the goal is fluency, not the complete absence of human speech patterns.
Name a custom list if you need one
If you have a verbal tic that is specific to you, or a word you use deliberately that keeps getting cut, say so and it will be targeted or spared accordingly.
Hide the visible joins
On a locked-off camera, each removed word leaves a small jump in the picture. A slight change of scale across the cut, or a cutaway over the busiest section, converts a jolt that reads as a mistake into one that reads as an edit.
Pro Tips for Best Results
- Restarts are the biggest win, bigger than um and uh combined. When you begin a sentence three times, only the last attempt belongs in the video.
- If the result sounds oddly inhuman, it is usually missing breath rather than missing words. Ask for more breathing room rather than putting the filler back.
- Match the intensity to the format: short-form can take everything out, while an interview should keep most of its texture.
- Generate captions after the cleanup, not before, so they match the edited audio exactly.
Restarts are the largest single source of clutter in unscripted speech — larger than um and uh combined — which is why removing them makes delivery read as fluency you did not actually have.
Frequently Asked Questions
Common questions around this editing workflow.