How to Remove Filler Words From Audio Recordings

A 60-minute interview recording typically contains 150-300 filler words like "um," "uh," "like," and "you know." Removing these manually requires careful listening and precise cutting, often taking 3-5 hours per hour of content.

Filler word removal is the process of identifying and deleting non-lexical utterances and verbal hesitations from audio recordings while preserving natural speech patterns and meaning. This process improves perceived professionalism and listener comprehension.

The Impact of Filler Words on Content Quality

Excessive filler words affect how audiences perceive and engage with content:

Listeners report 30-40% lower perceived credibility when speakers use frequent fillers
Comprehension decreases by 15-20% in content with high filler density
Audience retention drops measurably during segments with clustered fillers
Professional content typically has fewer than 2 filler words per minute

Research on speech perception shows that while occasional fillers are natural, more than 3-4 per minute begins to distract listeners from the content itself.

Common Filler Words to Remove

The most frequent filler words in English audio fall into several categories:

Hesitation Markers

Um and uh: Account for 40-50% of all fillers
Er and ah: More common in British English
Mm and hmm: Often used while thinking

Discourse Markers

Like: Can appear 20-30 times in casual conversation
You know: Often repeated unconsciously
I mean: Used to clarify or backtrack
So: Frequently overused as sentence starters

Verbal Pauses

Well: Common at the beginning of answers
Actually: Often unnecessary qualifier
Basically: Typically adds no meaning
Right: Used as confirmation seeking

Manual Methods to Remove Filler Words

Descript

Import audio for automatic transcription
Navigate to Edit > Remove Filler Words
Select which fillers to target (um, uh, like, etc.)
Preview identified instances
Apply removal and regenerate audio

Typical time: 2-3 hours per hour of footage, including review of each instance.

Adobe Audition

Listen through content and mark filler locations
Use spectral view to identify filler frequency patterns
Select and delete each instance individually
Apply crossfade to smooth transitions
Review edited segments for natural flow

Typical time: 4-6 hours per hour of footage.

Manual Transcription Method

Transcribe audio completely
Highlight all filler words in transcript
Note timestamps for each instance
Cut corresponding audio segments
Close gaps and review

Typical time: 5-7 hours per hour of footage.

Limitations of Manual Filler Removal

Manual identification and removal of fillers presents several challenges:

Listening fatigue: Editors become less accurate after 60-90 minutes of focused listening.

Inconsistent standards: What qualifies as "removable" varies by editor and context.

Time investment: Even experienced editors spend 2-4 hours per hour of content on filler removal alone.

Risk of over-editing: Aggressive removal can make speech sound robotic or unnatural.

Context sensitivity: Some fillers serve communicative purposes and shouldn't be removed.

For regular podcast or video producers, manual filler removal can consume 40-80 hours per month.

How Automatic Filler Detection Works

Modern automatic tools use speech recognition and pattern matching to identify filler words:

Audio is converted to text via speech-to-text engine
Algorithm identifies filler words in transcript
Timestamps map text fillers back to audio locations
Audio segments containing fillers are isolated
Segments are removed or shortened based on settings
Remaining audio is rejoined with smooth transitions

Detection accuracy varies by:

Audio quality: Clear recordings produce 85-95% accuracy
Accent and dialect: Systems trained on diverse speech perform better
Speaking speed: Rapid speech can cause 10-15% more missed detections
Background noise: Reduces accuracy by 15-25%

Configuring Filler Removal Settings

Effective automatic filler removal requires balancing thoroughness with natural sound:

Aggressiveness Levels

Conservative: Removes only clear, isolated fillers. Keeps content sounding natural but may leave some fillers. Typically removes 60-70% of detectable fillers.

Moderate: Removes most fillers while preserving speech rhythm. Removes 75-85% of fillers. Suitable for most podcast and video content.

Aggressive: Removes nearly all detected fillers. Can sound overly clean or slightly unnatural. Removes 90-95% of fillers. Works well for scripted or professional content.

Context Preservation

Some tools allow exceptions:

Keep fillers during emotional moments
Preserve fillers that indicate speaker thinking
Maintain fillers in quoted or reported speech
Retain fillers that serve grammatical functions

Combining Filler and Silence Removal

Many automatic editing workflows address both issues simultaneously:

First pass removes silence and dead air
Second pass identifies and removes filler words
Combined approach can reduce content length by 25-45%
Total processing time: 10-20 minutes for automatic tools

Tools like Rendezvous handle both silence and filler removal in a single automated pass. Users upload raw recordings and receive cleaned audio with both long pauses and common filler words removed. The combined approach typically reduces total editing time by 70-85% compared to manual methods.

When to Keep Filler Words

Not all filler words should be removed:

Authentic conversation: Casual podcasts may benefit from some fillers for natural feel.

Emotional emphasis: Hesitations can convey genuine thought or emotion.

Speaker characterization: Distinctive speech patterns may include recognizable fillers.

Pacing indicators: Some fillers signal important transitions or thinking moments.

Cultural authenticity: Certain fillers are characteristic of specific dialects or communities.

The goal is polished content, not perfect content. Removing 70-80% of fillers typically achieves the right balance.

Summary

Removing filler words from audio can improve perceived professionalism and listener comprehension. Manual removal takes 3-6 hours per hour of content, while automatic tools reduce this to 15-20 minutes including review.

Key considerations for filler word removal:

Focus on high-frequency fillers (um, uh, like, you know)
Use moderate aggressiveness settings for natural sound
Preserve fillers that serve communicative purposes
Combine with silence removal for maximum efficiency
Review automated results before publishing

For content creators producing regular podcasts or videos, automatic filler removal is a practical way to improve quality without proportional time investment.

Content reviewed on January 2026.