Remove Filler Words Automatically with Descript

Remove Filler Words Automatically with Descript

Every speaker uses filler words. "Um," "uh," "like," "you know," "sort of," "I mean" — these verbal placeholders appear naturally in unscripted speech as the brain searches for the next thought. In casual conversation they pass unnoticed. In a recorded podcast, YouTube video, or corporate interview, they accumulate quickly and can undermine the authority and polish of your content.

Manually removing filler words from a long recording is one of the most tedious tasks in audio and video post-production. Listening through an hour-long interview, finding every "um," setting cut points, and deleting each one can add hours to an edit. Descript eliminates this problem almost entirely with its automated filler word removal tool — one of the most practical time-saving features in modern content production software.

Why Filler Words Matter

Research consistently shows that audiences perceive speakers with fewer filler words as more confident, knowledgeable, and credible. For podcasters and video creators building a professional brand, this is directly relevant to audience retention and subscriber growth. A tightly edited recording that flows smoothly keeps listeners and viewers engaged; a recording peppered with "um" and "uh" can feel unpolished and harder to follow.

Filler words also inflate runtime. A 40-minute raw recording with frequent hesitations might trim down to 34 or 35 minutes once the filler is removed — that is meaningful pacing improvement, and the saved time can be given back to genuinely valuable content.

How Descript Detects Filler Words

When you import a recording into Descript and the transcription completes, Descript's AI has already analyzed the speech patterns in your audio. Filler words are flagged in the transcript with special highlighting — by default they appear in a distinct color, making them immediately visible as you read through the document.

The detection model has been trained to recognize the most common filler words in English (um, uh, like, you know, so, actually, basically, literally, right) and can be extended to catch custom words and phrases that you specify. If a particular speaker you work with frequently says "kind of" or "at the end of the day," you can add these to the detection list.

Descript distinguishes between filler uses and legitimate uses of the same words. "Like" as a filler ("it was like, really impressive") is flagged, while "like" as a preposition ("it looked like a painting") typically is not. Accuracy is high but not perfect, and you should always review the flagged words before committing to deletion.

Step-by-Step: Removing Filler Words

Step 1: Open your project and complete transcription. Import your audio or video file and wait for Descript's transcription to finish. Once the script view is open, you are ready to proceed.

Step 2: Open the Filler Words panel. Navigate to the Actions menu and select Remove Filler Words, or use the dedicated button in the toolbar if it is visible in your workspace layout. This opens the filler word control panel.

Step 3: Review detected filler words. The panel will display a count of each type of detected filler word in the recording. Before removing anything, use the "Preview" function to step through each detected instance. This lets you verify that the AI has correctly identified filler uses versus legitimate uses.

Step 4: Select which words to remove. You can toggle each filler word category on or off. If you are confident about removing all "um" and "uh" instances but want to manually review "like," uncheck "like" before proceeding.

Step 5: Apply the removal. Click Remove (or Apply) and Descript will delete all selected filler word instances from the transcript and the underlying media simultaneously. The edit is non-destructive — Descript uses its scene-based editing model, so original media is never permanently deleted and you can undo the operation at any time.

Step 6: Listen back and fine-tune. Play through the edited recording, particularly around the removal points. In some cases, a removed "um" may leave a slightly abrupt transition. You can manually smooth these with Descript's gap removal feature, which fills micro-silences with natural-sounding room tone, or by manually adjusting the edit point in the timeline view.

Handling the Gaps

When a filler word is removed, it leaves a short gap of silence in the audio. Descript handles this automatically in most cases through its gap removal system, which detects and closes pauses below a threshold you can set. For a natural-sounding result, a gap removal threshold of around 0.3–0.5 seconds works well for most speech content — short enough to remove awkward pauses without making the speech feel unnaturally rushed.

Custom Filler Words

Beyond the default list, you can add any word or phrase to Descript's filler word detector. This is useful when working with speakers who have personal verbal habits. Open the filler word settings and type in the custom terms. Descript will scan the existing transcript and flag all instances, which you can then review and remove as usual.

Exporting a Clean Recording

After filler word removal, export your project as you normally would — MP4 for video, MP3 or WAV for audio-only podcasts. The exported file will reflect all edits including the filler word removals, and Descript will regenerate any caption or SRT files based on the updated, cleaned transcript.

For podcasters producing multiple episodes per week, this feature alone can justify the cost of a Descript subscription. What used to require a dedicated audio editor listening through every minute of raw recording can now be handled in a fraction of the time, freeing you to focus on content quality and creative decisions rather than mechanical cleanup.