Guide to How to find and fix filler words using Artificial Intelligence voice tools

How to Find and Fix Filler Words Using Artificial Intelligence Voice Tools

A step‑by‑step tutorial that lets you clean up speech recordings fast, without the guesswork.

Introduction

Every speaker drops words like “um”, “you know”, or “like” without noticing. Those filler words make recordings sound unprofessional and can hurt SEO when you publish podcasts or videos. Thanks to AI voice tools, you can automatically spot and remove filler words, saving hours of manual listening.

What Counts as a Filler Word?

Filler words are short, often repeated sounds or phrases that do not add meaning. Typical examples include:

• um / uh
• you know
• like
• so
• actually
• basically
• right?
• okay

Identifying them correctly is the first step toward a polished audio file.

Why Remove Filler Words?

Removing filler words:

1. Improves listener retention.
2. Boosts perceived authority and confidence.
3. Reduces file size modestly, helping page‑load speed.
4. Enhances SEO when transcripts are cleaner.

AI Voice Tools That Detect Fillers

Tool	Key Feature	Pricing
Google Cloud Speech‑to‑Text	Word‑level timestamps + confidence scores	Pay‑as‑you‑go (~$0.006/15 s)
OpenAI Whisper (API)	Multilingual, high‑accuracy transcription	Free tier then $0.006/min
Descript Overdub	Built‑in filler‑removal button	$12/mo (Pro)
AssemblyAI	Automatic filler detection via “auto‑punctuate”	$0.025/min

Step‑by‑Step: Find Fillers with AI

Tip: Use word‑level timestamps to locate filler words precisely.

1. Upload your audio. Most cloud services accept MP3, WAV, or OGG.
2. Run transcription. The example below uses Python with the Whisper API.
3. Parse the transcript. Search for a pre‑defined filler list.
4. Export timestamps. You’ll receive start‑ and end‑times for every filler.
5. Cut or mute the sections. Use FFmpeg to automate removal.

Code Example: Detect & Remove Fillers

# Install required packages
# pip install openai ffmpeg-python tqdm

import openai, json, re, os
from tqdm import tqdm
import ffmpeg

# 1️⃣  Set your OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY")

# 2️⃣  Define filler words (add or remove as needed)
FILLERS = r"\b(um|uh|you know|like|so|actually|basically|right|okay)\b"

def transcribe(file_path):
    with open(file_path, "rb") as f:
        response = openai.Audio.transcribe(
            model="whisper-1",
            file=f,
            response_format="verbose_json",   # gives word‑level timestamps
        )
    return response

def extract_filler_timestamps(transcript):
    timestamps = []
    for segment in transcript["segments"]:
        text = segment["text"]
        for match in re.finditer(FILLERS, text, flags=re.IGNORECASE):
            # Whisper returns start & end for each segment,
            # we approximate filler position inside the segment.
            start = segment["start"] + (match.start() / len(text)) * (segment["end"] - segment["start"])
            end   = segment["start"] + (match.end()   / len(text)) * (segment["end"] - segment["start"])
            timestamps.append((round(start, 2), round(end, 2), match.group()))
    return timestamps

def cut_fillers(in_file, out_file, filler_times):
    # Build a filter string that skips filler intervals
    filter_parts = []
    prev_end = 0.0
    for start, end, _ in filler_times:
        filter_parts.append(f"between(t,{prev_end},{start})")
        prev_end = end
    filter_parts.append(f"between(t,{prev_end},INF)")
    filter_expr = "+".join(filter_parts)

    (
        ffmpeg
        .input(in_file)
        .filter_("aselect", filter_expr)
        .filter_("asetpts", "N/SR/TB")
        .output(out_file, **{"c:a":"aac"})
        .run(overwrite_output=True)
    )

if __name__ == "__main__":
    audio = "raw_speech.mp3"
    print("Transcribing…")
    result = transcribe(audio)
    filler_times = extract_filler_timestamps(result)
    print("Found filler words:", filler_times)
    print("Generating cleaned file…")
    cut_fillers(audio, "cleaned_speech.mp3", filler_times)
    print("Done – cleaned file saved as cleaned_speech.mp3")

      

The script does three things:

• Calls Whisper to get a detailed transcript with timestamps.
• Uses a regular expression to locate filler words.
• Creates a new audio file where filler intervals are omitted using FFmpeg.

Alternative: Auto‑Mute with Descript

If you prefer a UI, Descript’s “Filler Removal” button automatically detects and mutes listed words. The workflow:

1. Import your audio into a new project.
2. Click Filler Removal in the toolbar.
3. Review the highlighted filler sections and confirm.
4. Export the cleaned audio.

Descript also writes a clean transcript, which boosts SEO when you embed it on a page.

Best Practices for a Polished Result

• Keep a custom filler list. Different speakers use unique habits.
• Review the auto‑edited file. AI can mis‑label short breaths as “um”.
• Maintain natural pacing. Avoid chopping too many fillers in a row; it may sound robotic.
• Update transcripts. Replace removed words in the text to keep sync.

Conclusion

AI voice tools let you locate and eliminate filler words in seconds, turning a raw recording into a professional asset. Whether you code your own pipeline with Whisper and FFmpeg or use a drag‑and‑drop solution like Descript, the core process stays the same: transcribe, pinpoint filler timestamps, and cut them out cleanly. Implement the steps above, tailor the filler list to your voice, and watch listener engagement—and SEO—rise.

Ready to give your audio a cleaner edge? Start by testing the Python script on a short clip, then scale up to your full podcast library.

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