How to Remove Filler Words From an Interview

Remove filler words fastest by working from a transcript instead of scrubbing the video timeline: mark every um, uh, and false start in the text, judge discourse markers like "you know" and "I mean" case by case instead of deleting them all, then let word-level timecodes cut the audio and video together in one pass.

Why It Matters

Filler Words Cost Credibility, Not Just Polish

This is not just an aesthetic preference. In research on vocal fillers, credibility, and likeability published by Duvall, Robbins, and colleagues, speakers who used more vocal fillers were rated as less credible by listeners. Toastmasters International runs a formal role built around this exact problem: the Ah-Counter. As the organization puts it, "the purpose of the Ah-Counter is to note any overused words or filler sounds used as a crutch by anyone who speaks during the meeting," a description from the Toastmasters magazine. The same crutch words that cost a public speaker credibility in a room cost a video its watch time on a screen. Coverage in Forbes on the neuroscience of filler words makes a similar point: listeners process filler-heavy speech as more effortful, and that extra effort quietly erodes trust in the speaker even when the actual content is solid.

Not Zero

Some Filler Serves the Speaker

The goal is not eliminating every filler sound, which is not realistic and can flatten a speaker's natural cadence into something robotic. The goal is getting fillers below the point where they distract, while leaving the discourse markers that carry real meaning or rhythm.

What To Cut

Non-Words First, Then Judgment Calls

Start with the easy cuts: um, uh, er, and repeated false starts where a sentence restarts mid-thought. Those almost never carry meaning and almost always improve the cut when removed. Discourse markers like "you know," "I mean," "like," and "so" are a different category, sometimes they are pure filler, sometimes they carry rhythm or signal a shift in thought. Cutting every instance on autopilot tends to make a speaker sound stiff and over-processed, so these need a listen, not a find-and-replace.

Setting a Threshold

How Aggressive to Be Depends on What You Are Protecting

Before removing a single word, decide what the cut is optimizing for. A tight, confident delivery, or a natural, unscripted feel. Those pull in opposite directions once removal gets aggressive. A reasonable starting threshold is removing every clear non-word filler and every false start, then listening back once before deciding whether the surviving discourse markers help or hurt. A simple test that works in practice: read the cleaned line back in your head at normal speaking pace, if it still sounds like a real person talking, the threshold was right; if it sounds like a script, it was cut too hard.

Different Content, Different Rules

A Sales Video and a Documentary Need Different Thresholds

A corporate explainer or a sales video usually benefits from aggressive filler removal, viewers read a tighter cut as more confident and more competent, consistent with the credibility research above. A documentary interview or an emotional testimonial can go the other way, natural pauses and the occasional "I mean" often read as authenticity, and stripping all of it out can make a real person sound scripted. A social clip pulled for reach sits somewhere in between, tight enough to hold attention in the first few seconds but not so scrubbed that it feels like a script read off a teleprompter. Match the aggressiveness of the cleanup to the format, not a fixed rule applied everywhere.

Automated Detection

What Filler-Detection Tools Actually Catch

Automated filler detection works by pattern-matching common non-word sounds and known discourse markers against the transcript, which is reliable for um, uh, and the obvious repeats, and much less reliable for judgment calls. A tool can flag every instance of "like" in a transcript, but it cannot tell you which ones are filler and which one is doing real grammatical work in the sentence. That is why every automated pass still benefits from a human skim before the cuts get applied, the tool narrows the list, it should not make the final call alone on anything beyond the clearest non-words.

Language and Accent

Fillers Are Not the Same Sound in Every Language

Filler removal gets more complicated on multilingual or heavily accented content because the fillers themselves are not universal. Linguists have documented language-specific filler patterns for decades, French speakers commonly use "euh," Japanese speakers use "eto" or "ano," Spanish speakers lean on "o sea" or "este," alongside the same basic function English's um and uh serve. A transcript-based workflow handles this better than a generic audio filter tuned only to English filler sounds, since the fillers get marked as text in whatever language they were actually spoken in, rather than relying on an audio model trained mostly on English speech patterns.

One Pass Method

Mark It Once, in the Transcript, With Timecode

Work from a transcript with word-level timecode attached to the footage. Read through once, marking fillers and false starts as you go, then remove the marked words in bulk instead of hunting one at a time on a timeline. Because each word carries its own timecode, the cut audio and video stay in sync automatically, there is no separate step to re-align picture to sound after the text changes. ScriptCut's Remove Fillers feature is built around exactly this workflow.

How Tools Compare

Not Every Filler Removal Tool Works the Same Way (as of July 2026)

Pairing With Other Cleanup

Filler Removal Is One Pass Among Several

Filler removal rarely happens in isolation, it usually runs alongside trimming dead silence, tightening long pauses, and cutting repeated takes where a speaker restarted an answer entirely. Doing all of these from the same marked transcript, rather than as separate passes on the timeline, keeps the cuts consistent, a pause trimmed to match the pacing set by the filler removal reads as intentional, while a pause left at its original length right next to a tightly cut filler removal can feel jarring and uneven.

Worked Example

A 30-Minute Interview, Two Very Different Timelines

A 30-minute interview typically runs somewhere around 150 to 250 filler instances depending on the speaker. Removing those by scrubbing the video timeline, finding each one, marking an in and out point, and cutting it, realistically takes the better part of an afternoon. Doing the same removal from a marked transcript, where the words are already flagged and the cuts apply in bulk, typically takes about fifteen to twenty minutes, plus a listen-through to catch anything the pass missed.

Common Mistakes

Where Filler Removal Goes Wrong

• Working on the timeline instead of the transcript. Slowest possible method, and the easiest way to miss instances buried in long takes.
• Removing every discourse marker on principle. Strips out natural rhythm and can make a speaker sound artificial, especially bad for testimonials and documentary interviews where authenticity matters.
• Trusting automated detection without a human pass. Pattern-matching catches obvious non-words reliably, but not the judgment calls on discourse markers.
• Applying one threshold to every project. A sales video and a documentary interview call for different levels of aggressiveness, not the same rule copied across formats.
• Skipping the verification pass. A removed filler in continuous, static footage can leave an audible jump or a visible splice; always play back the cut before calling it done.
• Applying the same aggressiveness to every speaker. A naturally fast, filler-heavy talker and a naturally measured one need different thresholds, not one blanket rule.

The Takeaway

The Transcript Is the Fast Path, Not the Timeline

Filler word removal is a text problem wearing a video costume. Do it on the transcript, keep word-level timecode attached so the cuts land cleanly, set a threshold that matches the format, and use judgment on discourse markers instead of stripping everything that is not a hard noun or verb.

Sources

• The Effect of Vocal Fillers on Credibility, Communication Competence, and Likeability (Cal Poly)
• Counting on the Ah-Counter (Toastmasters International)
• The Neuroscience of Filler Words (Forbes)
• Descript pricing