Intelligent Filler Word Removal

Most automated filler word removal treats every instance the same. Remove all "ums," delete all "uhs," strip out every "like." But blanket removal creates a new problem: it strips out meaningful language along with the verbal tics. The result sounds robotic instead of professional.

The difference between good editing and great editing isn't just what you remove — it's knowing when to leave things in.

The "Like" Problem

"Like" is one of the most common filler words in conversational speech, but it's also one of the most legitimately useful words in English. Removing every instance breaks sentences that were perfectly fine.

Remove "like" when it's filler

• Hesitation filler: "It's, like, really important" → "It's really important"
• Approximation filler: "like a week or so" → "a week or so"
• Verbal tic: Random "like" dropped in with no semantic value

Keep "like" when it's meaningful

• Comparisons: "templates are like a house" — essential metaphor
• Preferences: "I like app notifications" — expressing opinion
• Examples: "something like an add-to-workflow" — giving examples
• Transitions: "Like I said..." — conversational connector
• Idiomatic phrases: "anything like that" — natural phrasing

Real Results

Applying this context-aware approach to a 57-minute training recording:

What	Result
Original length	57 minutes
After cleanup	45 minutes
True fillers removed	370+ (um, uh, you know)
Long pauses removed	142
Stutters removed	Single-word repeats (if, if / and, and)
"Like" instances	~60% removed, 40% preserved

That's 12 minutes of dead weight gone without losing any meaning. The content sounds tighter and more professional but still sounds like a real person talking.

The Framework

When you hit a filler word, ask one question: does removing this change the meaning or break the flow?

If it's a speed bump — cut it. If it's doing a job (comparing, exemplifying, expressing preference) — leave it. Speech should sound natural and conversational, not like it was run through a word filter.

Tools

Descript already handles the obvious stuff — ums, uhs, dead pauses. Its Underlord AI features can strip those automatically and the results are solid.

But nobody wants to go through a transcript line by line deciding which "likes" to keep and which to cut. That's tedious, manual work that defeats the purpose of AI editing. The real goal is AI that understands this nuance natively — that knows "templates are like a house" is a metaphor worth keeping, while "it's, like, really important" is dead weight.

Descript is probably the closest to cracking this. The distinction between a filler "like" and a meaningful "like" is a genuine language understanding problem, and it matters. Blanket removal sounds robotic. No removal sounds unpolished. The sweet spot — context-aware removal — is where AI editing needs to get to.

See Also

• Video Editing — Descript and other editing tools
• Video Clipping and Repurposing — From raw footage to finished content pieces