How to Remove Filler Words from a Transcript

You can safely remove ums, uhs, and other fillers by finding them precisely, checking each match in context, and re-exporting a cleaned transcript or captions.

Why remove filler words at all?

Filler words (um, uh, er, like, you know, I mean, sort of, kind of) make transcripts harder to skim and captions noisier to read. Cleaning them:

• Improves clarity and readability
• Tightens quotes for articles and show notes
• Produces cleaner training data or documentation
• Keeps subtitles concise on screen

That said, don’t strip authenticity or meaning. Some fillers act as discourse markers (“I mean,” “you know”) or carry tone. Remove only what doesn’t change intent.

What you’ll need

• A transcript file (TXT, DOCX, SRT, VTT) or the source audio/video
• A text editor that supports find-and-replace (regex optional)
• A plan for review so you don’t delete meaningful words by accident

If you don’t have a transcript yet, upload your file or paste a URL into the audio-to-text tool. It supports many languages and produces speaker labels, an AI summary, and action items. You can export to TXT, SRT, VTT, and more for editing.

The safe, fast workflow (step-by-step)

1. Get a transcript you can edit

• Already have one? Make a local copy.
• Need one? Upload your media or paste the page link into the audio-to-text tool, then export TXT if you want to edit plain text, or SRT/VTT if you must work in captions.

2. Duplicate the file
   Work from a duplicate so you can compare “before vs after” or restore anything you remove.

3. Build a filler-word checklist for your speakers
   Start with common English fillers and tailor to the accent or language in your audio. Examples:

• um, uh, er, hmm, mm, mhm (note: “mhm” can mean yes — evaluate context)
• like (as a filler), you know, I mean, sort of, kind of, basically, actually
• repeated restarts: “I— I”, “we- we”, and elongated sounds: “ummm”, “uhhh”

For other languages, list the equivalent fillers your speakers use. Because CATT supports many languages, you can run the same process for each transcript.

4. Choose your approach
   You can remove fillers manually or in batches. Pick based on your comfort and file size:

5. Start with the safest terms
   Remove only the unambiguous fillers first (um, uh, er). Use whole-word searches so you don’t erase parts of other words.

• Search pattern ideas:
  • Whole word “um” surrounded by spaces or punctuation: space + um + space/comma/period
  • Variants: um, umm, ummm; uh, uhh, uhhh; er, err
• Replace with:
  • A single space if found between words
  • Nothing if found next to punctuation (and then normalize spacing)

Tip: Run “Find next” and confirm a few matches before using “Replace all.”

6. Handle “like,” “you know,” and other context-sensitive fillers
   These can be meaningful. Use targeted passes:

• “like” as a filler:
  • Focus on cases near commas or pauses: “, like,” “like,” at clause breaks
  • Avoid verb uses (“I like this”, “it looks like rain”)
  • If your editor supports word boundaries, match just the word “like” and review each hit
• “you know,” “I mean,” “sort of,” “kind of”:
  • Safest when they stand alone before or after a clause
  • Skip when they change intent or soften claims (use editorial judgment)

7. Clean stutters and repeated starts
   Common patterns:

• Duplicated words with dashes: “I- I”, “we- we”
• Doubled words with spaces: “I I”, “we we”
• Elongations: “ummm”, “uhhh”

Suggested passes:

• Replace “I- I” with “I”, “we- we” with “we”, etc.
• Replace multiple spaces with a single space
• Replace “ummm” and “uhhh” with nothing (then fix spacing)

8. Normalize spacing and punctuation
   After removals, fix readability:

• Convert multiple spaces to one
• Remove spaces before commas and periods
• Ensure one space after punctuation
• Merge orphan punctuation: “, ,” → “,” and “. .” → “.”

9. Preserve speaker intent and attributions

• Keep speaker labels from your transcript so quotes remain attributable
• If CATT diarization labeled speakers, retain or adjust labels where line breaks changed
• Read tricky sentences aloud — if meaning shifts, undo that deletion

10. Re-export clean text or rebuild subtitles

• If you edited a plain transcript: export/share as TXT or DOCX
• If you started from SRT/VTT and made many deletions:
  • Consider regenerating subtitles from the source media so timing recalculates cleanly. You can create new captions from your video with the subtitle generator.
  • Alternatively, re-run transcription and export SRT/VTT again from the audio-to-text tool after your cleanup steps.

If you prefer to work directly where you transcribe, try cleaning inside a text-first workflow with the text-based audio editor, then export to TXT, SRT, or VTT.

Practical search patterns that avoid false positives

You don’t need advanced regex to be careful, but a few conventions help:

• Whole-word matches: Target standalone fillers so you don’t catch substrings. Examples:
  • Match “ um ” (spaces on both sides)
  • Match “, um ” (comma, space, word, space)
  • Match “ um.” (space, word, period)
• Variants in one pass: Group close spellings for efficiency.
  • um|umm|ummm
  • uh|uhh|uhhh
• Near-punctuation patterns: For discourse markers, target common placements.
  • “, like,” “, you know,” “, I mean,”
• Review-first workflow:
  • Do a single “Find next” lap through a file to learn its quirks
  • Then run careful “Replace all” only for the safest terms

If your editor supports regex with word boundaries, you can craft patterns that match only entire words and common punctuation neighbors. Always test on a small section first and keep your backup file handy.

Special considerations for captions (SRT/VTT)

Working inside SRT or VTT requires extra care:

• Timestamps won’t update themselves
  • Deleting words in a caption line won’t shift the cue time. Large edits can cause lines that feel early or late.
• When to edit SRT/VTT directly
  • Light cleanup (removing an isolated “um”) is usually fine
  • Heavy cleanup is better done in a plain transcript, then regenerated
• Safe workflow
  1. Export a plain transcript and clean it thoroughly
  2. Regenerate subtitles from the source media so timing is recalculated, or re-export captions after transcription
  3. Spot-check a few cues against the audio to confirm sync

If you need a fresh set of captions, generate them from your video with the subtitle generator, or transcribe and export SRT/VTT again via the audio-to-text tool.

Keep the human voice while you clean

Not every filler needs to vanish. Use judgment:

• Preserve emphasis or tone markers when they matter (“well,” “so,” sometimes “I mean”)
• In interviews, keep enough texture to reflect the speaker’s style
• For legal or research transcripts, follow your style guide about verbatim vs. cleaned verbatim

If you’re unsure you removed too much, produce a quick summary and see if the key points still read the same. The audio summarizer can help you compare the gist of the original vs. cleaned version.

Example mini-checklist you can reuse

• Safe batch deletions:
  • um, umm, ummm
  • uh, uhh, uhhh
  • er, err
• Review in context:
  • like, you know, I mean, sort of, kind of, basically, actually
• Fix-ups:
  • Double spaces → single space
  • Remove spaces before commas/periods
  • Merge duplicated words (“I I” → “I”)

Quality assurance: a 5-minute spot-check

• Read 3 random paragraphs out loud — do they flow naturally?
• Search “like” and spot-check 10 hits — did any verbs get removed?
• If you have captions, play 1 minute of video — do lines appear at natural breaks?
• Confirm speaker labels still align with content
• Export final files: TXT for blogs/notes; SRT/VTT for video

When to stop editing

Over-cleaning can make people sound robotic. If the transcript is clear, consistent, and faithful to the speaker’s intent, it’s clean enough. Save the pristine version, and keep your untouched backup for reference.

Wrap-up

Removing filler words is mostly about precision and restraint: target only the real fillers, review context, normalize spacing, and re-export. If you need a transcript first, upload your media to the audio-to-text tool, clean the text (the text-based audio editor is handy), and regenerate captions with the subtitle generator when timing matters.

Practical takeaway: Build a tailored filler list, remove the easy wins first (um/uh), treat context-sensitive phrases with care, and always keep a backup so you can revert if meaning shifts.