Um detector. Upload a recording and AI counts every 'um', 'uh', 'like', and filler word with frequency stats.
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Filler Word Detector is an AI-powered tool that identifies and counts speech fillers and hesitation patterns in audio recordings. It detects common filler words like 'um', 'uh', 'like', 'you know', 'so', 'well', and other hesitation patterns, reporting frequency and usage patterns. The tool analyzes speech to identify all instances of filler words, counts the frequency of each type, calculates overall filler word rate, and notes patterns in their usage. It provides detailed feedback on filler word usage with suggestions for reducing fillers if they occur frequently. This makes it valuable for public speakers improving their delivery, professionals refining presentation skills, students developing speaking abilities, or anyone wanting to reduce filler words and speak more confidently and clearly.
Upload your speaking audio and the AI analyzes speech for filler words and hesitation patterns systematically. It identifies all instances of common filler words including 'um', 'uh', 'like', 'you know', 'so', 'well', 'actually', 'basically', and other hesitation markers. The tool counts the frequency of each filler word type, calculates the overall filler word rate (fillers per minute or per 100 words), identifies patterns in filler word usage (when and where they occur), and notes hesitation patterns beyond just words (pauses, repetitions, false starts). The analysis provides detailed statistics showing which filler words are used most frequently, when they tend to occur (beginning of sentences, transitions, etc.), and overall usage rate. It offers specific suggestions for reducing filler words, such as pausing instead of filling silence, preparing transitions in advance, and practicing smoother speech flow. You can specify particular filler words to track in the notes field.
Every instance of the classic fillers (um, uh, like, you know, so, well, actually, basically) plus hesitation patterns beyond words: false starts, repetitions, and stalling pauses. Each filler gets its own count, and the report shows where they cluster, like sentence openings or transitions between points.
There's no magic number, but listeners forgive occasional fillers and notice patterns. A filler every couple of sentences reads as natural; several per sentence reads as unprepared. The report gives your per-minute rate and your dominant filler so you can compare recordings over time, which matters more than any universal threshold.
Fillers buy planning time: they fill the gap while your brain assembles the next phrase, which is why they spike at transitions, complex points, and the starts of answers. That's also the fix. The detector's suggestions center on replacing the filler with a silent pause, which buys the same time without the static.
Very good on clear single-speaker audio; counts get fuzzier with crosstalk, heavy background noise, or mumbled delivery. The genuinely hard case is words like like and so, which are sometimes fillers and sometimes load-bearing; the analysis judges by context and mostly gets it right, but treat exact counts as close approximations.
Unscripted speech, because that's where fillers live: a meeting recording, a practice answer to an interview question, a voice memo where you explain something off the cuff. Two to five minutes gives stable statistics. Scripted reads will flatter you and tell you nothing about how you actually speak.
Counting is most of the battle: most people have one dominant filler they're blind to, and learning that it's you know at every transition makes it catchable in the moment. The report adds targeted suggestions (pausing instead of filling, pre-planning transitions), and retesting weekly shows whether the rate is actually dropping.
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