The difficult part of deleting "uh" is deciding whether it is a filler or the sound before a word you are still trying to find. Record five minutes of yourself and inspect the transcript. You will probably see "um," "uh," "you know," "kind of," "sort of," and "I mean" more often than expected.
That pattern does not mean you speak badly. It means you speak as a human does. Filler words support the mental work of spoken language. Once you understand their job, you can get cleaner dictation without treating ordinary speech as a flaw.
What filler words actually are
Fillers have a practical role in conversation. They tell the other person that your turn is still in progress while you think. Linguistic research describes "um" and "uh" as disfluencies that help manage the timing and rhythm of spoken exchange. They are functional signals, not meaningless noise.
During a live conversation, listeners generally recognize those signals and move past their literal content. Written text does not offer the same benefit. The words take up space, interrupt the line of thought, and add little meaning. A device that helps speech can therefore make a transcript harder to read.
How Wispr Flow decides what to remove
Any removal tool has to account for context because one expression can serve two different purposes. In "so, you know, I was thinking about it," "you know" acts as a filler. In "share this with everyone you know," it carries meaning. Likewise, an "uh" before a proper noun may differ from an "uh" inserted while you search for a word.
Wispr Flow uses context instead of deleting every flagged expression. The model looks at nearby words, the position of a possible pause, and the effect removal would have on the sentence's meaning. That judgment usually turns rough spoken input into prose that feels edited while retaining the speaker's idea.
Automatic cleanup still has limits. Sometimes a filler remains, and sometimes a meaningful word gets changed. The target is not perfect judgment on every sentence. It is a lower editing burden than a raw transcript, and contextual handling performs well against that practical standard.
What you can do to produce cleaner dictation
Software handles much of the cleanup, but speaking habits matter too. Producing fewer fillers at the source gives the system less to sort through and leaves you with a cleaner draft from the start.
Start with a pause. When the next phrase is not ready, stop for a moment rather than saying "um." Wispr Flow treats brief silence as a normal thinking pause. A half-second without sound is easier to remove from the finished text than a half-second of "uh." The quiet can feel awkward at first, especially in a voice interaction, but repeated practice makes it feel ordinary.
It also helps to use shorter sentence units. Long sentences create more places for fillers to appear. "The thing is, um, we need to, uh, figure out what the timeline looks like for, you know, the next sprint" contains four fillers in part because one sprawling sentence carries the whole thought. Three shorter sentences would be easier to say and would usually yield cleaner dictation.
When cleanup works against you
Automatic removal is not always desirable. A direct quote needs fidelity to the source, so raw output may be the better choice. Deliberately casual writing can also depend on "you knows" and similar phrases for its voice. Removing them too aggressively can make the text sound unlike the speaker.
Wispr Flow's profiles let you vary cleanup intensity. A lighter profile suits casual writing, while a heavier profile fits polished documents. Choosing between them is a judgment call, and regular use over a week or two makes that choice easier.
The balance this points to
Removing fillers cannot organize confused ideas. It can produce text without "ums" that is still unclear if the underlying thinking is unclear. Its useful role is narrower: it keeps the transcription medium from hiding the quality of your ideas. Clearer thinking gives the tool more to work with, and practicing deliberate pauses improves dictation before any AI processing begins.