How to Remove Filler Words from Video in Premiere Pro (um, uh, like)

Every creator knows the problem. You record a great take, then watch it back and count seventeen "um"s in the first two minutes. Or worse: a mid-sentence "so, like, what I'm trying to say is" that derails an otherwise tight explanation.

Filler words are different from silence. Silence removal cuts dead air. Filler word removal cuts spoken content that weakens your video — the ums, uhs, likes, you knows, and the full restarted takes where you said the same sentence twice before getting it right.

In Premiere Pro, there are three real approaches in 2026. This guide covers all three with honest tradeoffs.

What counts as a "filler word"?

For practical video editing purposes, there are three categories:

Category 1: Pure fillers (safe to always cut)

• um, uh, er — thinking sounds with no semantic content
• hmm — when used as a filler, not as a reaction
• ah — same as above

Category 2: Verbal habits (cut carefully)

• like — dangerous. Used as filler ("it was, like, really fast") but also legitimately ("it looks like a square")
• you know — often filler at the end of sentences, but can be conversational intent
• basically, literally, actually — frequently meaningless but sometimes genuinely emphasizing a point
• right? — used as filler confirmation, but can be a genuine question

Category 3: Restarted takes (the hard ones)

• "So the thing is — actually, let me back up" — speaker abandoned a sentence
• "The key point here is X. Sorry, what I mean is X" — rephrased the same idea
• "Let me try that again. The key point is..." — explicit restart

Category 3 requires AI detection. Simple keyword filters can't catch it. We'll get to that.

Method 1: Manual razor cuts (the brutal way)

The old-school approach: play through your timeline, press C to switch to the razor, cut before and after each filler, then delete the segment and close the gap with Shift+Delete (ripple delete).

How:

1. Open your sequence in Premiere
2. Play through the clip on your timeline
3. When you hear a filler word, stop playback (Space), jog back one second (← key)
4. Press C, click just before the filler word begins, click just after it ends — two razor cuts
5. Switch back to Selection tool (V), click the isolated filler segment, press Shift+Delete to ripple-delete
6. Repeat 60–200 times per video

The reality: This is how people were editing in 2015. For a 20-minute talking-head video with 80 filler words, expect 60–90 minutes of focused work. It's accurate, but you're essentially doing the same motion hundreds of times. And you still miss the restarted takes, which require you to also understand the meaning of what's being said to know what to cut.

Best for: One-off cleanup of short clips. Not scalable.

Time on a 20-min clip: 60–90 minutes

Method 2: Transcript-based keyword filtering

Several tools (Premiere's built-in Text panel, Descript, some extensions) let you search the transcript for filler words and delete them from a list view. This is significantly faster than manual razor cuts.

How (using Premiere's Text panel):

1. Window → Text → Transcript tab
2. Generate Transcript (Adobe Sensei, 1–2 min for a 20-min clip)
3. In the transcript, Ctrl+F to find "um"
4. Select the instances you want to cut, right-click → Delete
5. Repeat for "uh", "like", "you know", etc.

The reality: Better than razor cuts for pure fillers (um, uh), but has real problems:

• False positives on "like" and "you know" — you have to review each instance
• Sentence-level timestamps — Premiere's transcript editor doesn't always give you word-level timing, so cuts can clip the surrounding syllables
• Can't detect restarted takes — "Actually let me rephrase that" doesn't show up in a keyword search
• Manual review still required — you're scanning through a transcript list rather than building a list automatically

Best for: Editors who've already generated a Premiere transcript and want to knock out the obvious fillers in 15 minutes.

Time on a 20-min clip: 15–30 minutes (pure fillers only, no take detection)

Method 3: AI hybrid detection — EditBuddy

Modern CEP extensions run word-level transcription locally and then apply layered detection — rules for the obvious fillers, AI analysis for the hard cases.

EditBuddy's retake and filler detection works in four phases:

1. Phase 1 — System detection: Rule-based pass catches explicit fillers (um, uh, erm, hmm), meta-speech patterns ("let me try that again", "sorry", "actually"), and prefix chain abandonment (starting the same sentence stem twice in a row)
2. Phase 2 — AI clean script reconstruction: An AI model reads the full transcript and returns the "clean version" — what the speaker was trying to say, keeping the best take of each idea
3. Phase 3 — Inversion: The clean script is compared against the original. Anything in the original that isn't in the clean script is flagged as a cut candidate
4. Phase 4 — Merge: System cuts and AI cuts are combined with confidence scoring. High-confidence cuts (both agree) go in automatically. Lower-confidence cuts are flagged for review

The result lands directly on your Premiere timeline as actual cuts — no round-trip, no export. Your color grading, B-roll, and audio effects are untouched.

How:

1. Install EditBuddy (adds a panel to Premiere)
2. Open your sequence in Premiere
3. Window → Extensions → EditBuddy → click Auto Edit
4. Under Retakes, select your take strategy: Keep Last, Keep Longest, or Keep Best
5. Run. Filler words and retakes are cut in the same pass as silence removal

Why this approach wins over keyword filtering:

• Catches restarted takes. "So the key idea here — wait, let me rephrase this — the key idea is..." → only the final version kept. No keyword filter can do this.
• Context-aware "like" detection. "It looks like a square" (keep) vs "it was, like, really surprising" (cut). AI understands the difference.
• Word-level timing. Cuts land at exactly the right millisecond, not halfway through the surrounding syllable.
• Bundled with the full pipeline. Silence removal, filler word removal, captions, B-roll, and zoom all run in one pipeline pass. You don't need five separate tools.
• Automatic backup. Before any cuts are applied, Premiere creates a "{name} — Backup" sequence. If the result isn't right, revert in one click.

Time on a 20-min clip: 3–6 minutes total pipeline (all steps, not just filler detection)

Comparison table

	Manual razor	Keyword filter	EditBuddy AI
Pure fillers (um, uh)	✅ Manual	✅ Auto	✅ Auto
Verbal habits (like, you know)	✅ Manual	⚠️ Many false positives	✅ Context-aware
Restarted takes	✅ Manual	❌	✅ AI detection
Explicit restarts ("let me try again")	✅ Manual	⚠️ Partial	✅ Meta-speech rules
Word-level cut accuracy	✅ (with skill)	⚠️ Sentence-level	✅
Edits live Premiere timeline	✅	✅	✅
Automatic backup sequence	❌ Manual	❌	✅
Time on 20-min clip	60–90 min	15–30 min	3–6 min (full pipeline)

How to choose

Use manual razor cuts if:

• You're editing a 2–3 minute clip with a handful of obvious fillers
• You need surgical precision on a specific section
• You're in the final polish pass after everything else is done

Use keyword filtering if:

• You've already generated a Premiere transcript for captions
• Your speaker has very clean speech except for consistent "um" and "uh" habits
• You don't have many restarted takes in the footage

Use EditBuddy if:

• You edit weekly content and need filler removal as part of a repeatable pipeline
• Your footage has restarted takes, rephrasing, or mid-sentence corrections
• You want filler removal combined with silence cuts, captions, and B-roll in one pass
• You use the same workflow for multiple creators or clients

The hardest case: "like" and "you know"

These two words cause the most over-cutting problems. Here's a quick heuristic for each:

"like" — cut it when:

• It appears between a subject and a verb where it adds nothing: "It was, like, incredible"
• It's surrounded by pauses: "And so... like... the main thing is"
• The speaker says it 3+ times in one sentence

"like" — keep it when:

• It's a genuine comparison: "It works like a traditional NLE but with AI on top"
• It's followed by a direct object with clear intent: "People like this feature"
• Removing it changes the sentence meaning

"you know" — cut it when:

• It appears at the end of a sentence as trailing confirmation: "...which is pretty important, you know?"
• It's in the middle of a sentence as a pause filler: "The thing is, you know, it's complicated"

"you know" — keep it when:

• It's an actual question seeking confirmation: "You know how the timeline works?"
• It's part of a teaching phrase: "You know what I mean?" in a tutorial context

A simple keyword filter can't make these distinctions. That's why AI context-awareness matters for anything beyond pure fillers.

What about noises — breaths, clicks, lip smacks?

These are different from filler words. They're not words at all — they're sounds that appear between words. Standard transcript-based tools miss them entirely because they don't appear in the transcript.

EditBuddy has a separate noise detection pass: any segment where Whisper confidence is low AND duration is short (under 1.2 seconds) gets flagged as a candidate noise cut. This catches breath sounds, mic pops, and lip smacks that dB threshold-based silence detection misses.

If you only need breath removal without full pipeline automation, there are dedicated tools for that. But for a complete cleanup pass (silence + fillers + breaths + retakes), a single pipeline handles all four in the same run.

TL;DR

For a 20-minute talking-head video, manual razor cuts take an hour. Keyword filtering takes 15–30 minutes and misses the hard cases. AI hybrid detection via EditBuddy takes 3–6 minutes for the full pipeline — silence, fillers, retakes, captions, B-roll, and zoom in one pass.

If you're editing more than two videos per week in Premiere Pro, the math is easy. Free — one Auto Edit, no card required.

FAQ

Q: Can Premiere Pro remove filler words automatically?
A: Premiere's built-in tools don't detect fillers automatically. You need a third-party extension. EditBuddy uses hybrid AI + rule-based detection to find and cut filler words directly on your timeline without any export.

Q: What's the difference between filler word removal and silence removal?
A: Silence removal cuts dead air. Filler word removal cuts spoken-but-unwanted content — um, uh, like. You often need both. EditBuddy runs both passes in sequence.

Q: Will removing filler words make the video sound unnatural?
A: Done well, no. Removing clean fillers (um, uh) sounds natural. The risk is over-cutting every pause. EditBuddy leaves intentional pauses intact and uses confidence thresholds to avoid aggressive cuts.

Q: Does this work on interview footage with two speakers?
A: Yes. Filler detection runs on the full timeline transcript regardless of speaker count. For multi-cam podcast setups, Podcast mode handles speaker switching separately.

Q: What about "Let me try that again" and similar restarted takes?
A: Keyword filters miss those. EditBuddy's AI hybrid detection catches restarted takes using sentence similarity — it recognizes when the speaker says the same thing twice and keeps only the best version.