Remove filler words from your video

To remove filler words from a video, cut each "um", "uh", "like" or "you know" together with the small pause around it, then listen to the join, because a filler and a pause are not the same thing and both need a check before you commit to a cut. You can do it by hand in a timeline, in a transcript-based editor, or automatically: upload the raw recording, review the full watermarked preview, and ask for any pacing change in plain English.

A filler word and a pause are not the same thing

A filler word is an audible sound or phrase that fills a gap while you think: "um", "uh", "like", "you know", "so, basically", "I mean". A pause is silence — a beat with no sound at all, while you search for the next sentence, catch a breath, or let a point land.

Both happen naturally when someone talks off the cuff, and both can slow a recording down, but they show up differently in the waveform: a filler is a blob of sound, a pause is a flat, quiet stretch next to it. Treating them as two separate problems to fix in isolation is where most cleanups go wrong.

Cutting only the "um" and leaving the silent beat around it still leaves a hole in the pacing; cutting only the silence and leaving the "um" still leaves the sound. The unit worth reviewing is the whole moment — filler plus the pause on either side of it — not the word by itself.

Fillers tend to cluster at specific moments rather than spread evenly through a recording: right after a hard question, at the start of an answer before the first idea arrives, or when a speaker switches from one point to the next without a scripted line to fall back on. A pause, on the other hand, can show up anywhere a thought needs a beat to land, including places where removing it would flatten the delivery rather than improve it.

That difference is why a transcript alone does not settle the question. A transcript shows you where the filler words are, in text; it does not tell you which of the surrounding silences were doing useful work. Deciding that still means listening to the moment, not just reading it.

People also settle into their own habitual fillers — one speaker leans on "so", another on "right" or "actually" as a connector between sentences — which is worth knowing before a cleanup pass, since a word that's a filler in one speaker's mouth can be a genuine connective in another's. A single fixed list of "words to remove" misses that difference; the recording itself, not a dictionary, decides which instance is which.

What happens at the seam after you cut one out

A clean cut needs the audio on both sides of the join to match: background noise level, breath sound, and the speaker's volume have to be close enough that the splice doesn't produce an audible click or a sudden jump in loudness. A room with air conditioning running, or a mic placed closer on one take than another, makes this harder to get right than a quiet, consistent recording setup.

The cut also can't land mid-word. Trimming half a syllable off the word that follows a filler — the "I" in "um, I think" — is a common way a fix creates a new, smaller problem: a clipped consonant that reads as a stumble rather than a clean sentence.

On a vertical talking-head recording, the video track has to follow the same cut as the audio: lips, head movement and any captions need to stay lined up with the trimmed speech, or a viewer notices the mismatch even when the audio itself sounds fine. That's why the check that matters is a full preview of the result, not just a listen to an isolated audio clip.

A short crossfade — a few frames where the tail of one moment blends into the start of the next, instead of a hard cut — usually hides the seam better than a straight cut when the noise floor on either side isn't identical. Whether that's needed depends on the recording: a quiet room with a consistent mic level tolerates a hard cut; a room with background hum or a mic that picked up breathing needs the blend to avoid a click landing right where a viewer's ear is most sensitive.

When the pause comes from restarting the same line entirely, rather than a filler mid-sentence, see how to choose the intended take before you join the result.

Three ways to remove filler words: timeline, transcript editor, or automatic

Cutting a filler word by hand in a video editor's timeline means scrubbing the waveform, spotting each "um" or silent gap visually, setting in and out points around it, ripple-deleting the gap, and listening back to the join. It gives full control over every cut, and every minute of that control is spent by you, on your own timeline, one moment at a time — which adds up quickly once a recording has more than a handful of retakes.

A transcript-based editor turns the same job into reading rather than scrubbing: you see a text transcript of the recording, delete the words that are fillers, and the matching audio is cut to follow. Descript's own feature list names "Remove Filler Words" and "Remove Retakes" among its editing tools, on plans that start free and move to $24 a month, or $16 a month billed annually, for the paid tier (source: https://www.descript.com/pricing, checked 2026-09-17). It replaces waveform-scrubbing with reading and deciding on the page, but you are still the one who reads the transcript, decides which pause to keep, and applies each edit yourself.

An automatic pass works from the other direction: you upload the raw vertical recording once, with the fillers and pauses left in, and get back a version with them tightened. The check is the same either way — a full preview you can watch end to end — but the work of finding and cutting each moment happens before you see the result, and any pacing you want changed goes back as a plain-English note instead of a second pass through a timeline or a transcript.

Which route fits depends mostly on how much material there is and how much of the decision-making you want to keep for yourself. A thirty-second clip with one or two "um"s is a fair candidate for a quick manual trim. A recording with retakes, false starts and fillers scattered through several minutes is where reading or re-scrubbing every moment yourself starts to cost real time, and handing the first pass to an automatic tool — then reviewing the result — changes what you spend that time on.

The three routes also differ in how they're priced, which matters if this is a one-off rather than a recurring habit. ReelsCut charges per finished video — $5 for one, or $19 for a bundle of 15 a month, $29 for 30, $69 for 100 — instead of a recurring editing-software subscription. Whether a per-video price or a monthly plan fits better usually depends on whether there's one recording to clean up or a steady stream of them.

Where Riverside and Adobe Podcast fit

Riverside advertises an AI feature on its own site described as letting you "clean up filler words, silences, and fluff with one satisfying click" (source: https://riverside.com/, checked 2026-09-17) — a recording-and-editing platform built around the automatic route above, where you still open the result and confirm the cut yourself.

Adobe Podcast takes a different shape: its public pages describe an editor built around a transcript — "record, transcribe, and edit for free", treating audio like a document — plus a separate Enhance Speech filter aimed at background noise and echo (source: https://podcast.adobe.com/en/edit-with-adobe-podcast, checked 2026-09-17). Its listed features cover transcription, captions and noise cleanup; a dedicated one-click filler-word or pause remover is not among them, so cutting a filler there means deleting the word from the transcript by hand — the transcript-editor route described above, not the automatic one.

An AI pass that clicks through filler words in one click — Riverside's or any other — still has to guess where a filler ends and real speech begins, and a guess can be wrong on a word that only sounds like a filler out of context: a genuine "like" used as a comparison, or a "so" that starts a sentence rather than filling a gap. That's the reason the check that matters is a full preview of the finished result, not a report that says how many fillers were found.

If the result sounds choppy after cleanup

A cleanup that goes too far usually shows up as one of two things: the sentence feels rushed, like a breath was cut along with the filler, or two separate thoughts now run together because the pause that used to separate them is gone.

The usual cause is treating the filler and the pause around it as one cut without checking whether that particular pause was actually carrying something — emphasis before a key point, or a breath the speaker needed before the next sentence. It also shows up when several fillers sit close together: cutting each one on its own can leave a string of short, evenly spaced trims that reads as rushed even though every individual cut looked fine in isolation.

The fix is a specific request, not a general one: name the moment ("keep the pause before the second point", "the cut right after I say the product name feels rushed") rather than asking for the whole recording to sound "less choppy". A request tied to a timestamp or a quoted phrase from the recording gets a precise fix; a general request to "smooth it out" tends to come back changed in places that weren't the problem.

You have up to 2 revision rounds before payment and up to 10 more after the clean file is unlocked, so there is room to point at the exact spot and ask for it back rather than settling for a version that's close but not quite right.

What a clean recording returns to you

From a raw vertical recording with your fillers and pauses left in, you get back a finished short with selected takes, tightened moments where requested, and captions added on top.

The captions follow the cleaned speech, not the original recording, so a removed "um" doesn't leave a stray word on screen after the audio has already moved past it — the two tracks are trimmed together, not separately.

The framing stays vertical 9:16 with titles and captions kept inside the platform's safe zone, so the result is ready to publish without a separate rendering step or a second pass to fix layout.

Check the cut before you pay

You receive a full watermarked preview first, and you can watch it end to end. It is the actual result to review: use it to judge whether a filler, pause, speech alignment or caption needs another pass.

For a filler-and-pause cleanup specifically, that means listening past the spot where the cut happened, not just at it: does the sentence still land the way you meant it, does a caption flash by too fast because the words behind it were tightened, and does a pause you wanted kept still have room to breathe.

You pay only when you are satisfied with the cleaned version. After payment you get the clean, watermark-free video.

Tune the result with plain-English revisions

If the cleaning went further than you wanted — a pause you would rather keep, a sentence you wanted left looser — you say so in plain English and receive a fresh render.

You have up to 2 revision rounds before payment and up to 10 more after the clean file is unlocked, so you can review the real result and ask for a specific pacing change rather than guessing at a setting.

Descript is a trademark of its owner. ReelsCut is not affiliated with Descript.