The model has to tell an intentional pause from an "uh" before it can clean a transcript. That distinction is harder than it sounds: both may occupy the same moment in speech, yet one carries conversational meaning while the other only buys time.
Spoken language also contains repairs, hesitations, and abandoned openings that are ordinary in conversation but distracting on a page. Wispr Flow's cleanup pipeline therefore asks a narrow linguistic question: which words are disposable noise, and which belong in the writer's intended sentence?
Why Familiar Fillers Are Only the Starting Point
The classic hesitation sounds are relatively easy when considered alone. "Um," "uh," "ah," and "er" tend to occur in familiar positions and usually add no meaning. Removing them when the system recognizes that pattern handles the simplest part of cleanup.
"Like" demands more care. In "it was, like, three hours long," it works as a discourse filler. In "it felt like the project had stalled," it carries the comparison and must remain. Matching the word by itself cannot resolve that difference. The cleanup layer has to read the sentence around it.
"You know" and "right" create still more ambiguity. "You know how it is" expresses a meaningful idea, while "You know, the thing is..." may merely hold the floor. "Right?" at the end of a thought can act like punctuation, but "Right, so the next step is..." begins a new statement. The model needs context, not just a list of suspect words.
The Categories That Need More Reasoning
Several kinds of spoken content become difficult to classify once the system moves beyond familiar hesitation sounds.
False starts: Consider, "The meeting is, I mean, the meeting was scheduled for Thursday." The first clause contains valid words, but the speaker abandoned it and restarted. Leaving both versions in place makes the transcript look grammatically broken. Detecting the repair means recognizing that the speaker corrected the sentence while speaking.
Repetition: In "I think, I think we should move the deadline," the duplicated opening is probably accidental. Other repetition is less clear. "We looked at the data and the data was clear" could intentionally emphasize the subject, or it could reflect a repeated phrase. Nearby context is what separates emphasis from noise.
Trailing discourse markers: In "We finished the proposal, so, and the client should review it by Friday," the speaker loses momentum and starts again. The phrase "so, and" sounds awkward in writing and should be removed here. Yet both words serve legitimate grammatical roles in other sentences, so deletion cannot be based on vocabulary alone.
Inside Wispr Flow's Cleanup Layer
Wispr Flow uses a trained language model for cleanup rather than a fixed list of rules. Training uses paired examples: a raw transcript of natural speech on one side and an edited version of that same content on the other. Across thousands of examples, the model learns patterns that separate useful language from speech noise.
That approach can generalize beyond explicitly listed cases. A rules list would need a separate instruction for every filler pattern. The trained model instead learns structure, including discourse position, neighboring words, and whether a sound or phrase contributes meaning. When a new pattern appears, it can compare that situation with similar examples from training and make a reasoned probability-based choice.
There is a tradeoff: the choice can be wrong. The model does not possess certainty about an ambiguous phrase. It may occasionally delete language that should stay or preserve language that would have been better removed.
Why Errors Happen
Personal and regional speech habits make classification genuinely difficult. For one speaker, "You know what I mean" may be a habitual filler. For another, it may be a sincere check on shared understanding. The same is true of "Right?" as a closing marker, which varies enough by speaker that one universal rule performs poorly. The model relies on patterns in its training data, and those patterns cannot perfectly cover every person's idiom.
The way someone dictates also changes the problem. A practiced speaker tends to use cleaner sentence arcs and fewer repairs. A first-time dictator may produce more false starts and self-corrections, creating more unusual cases for the cleanup layer to interpret.
When the evidence is weak, the model favors preservation. Removing too much creates clipped, unnatural prose, while leaving some fillers produces a rough transcript. In our testing, users found the clipped version more disorienting, so low-confidence decisions lean toward keeping the content.
How Personalization Develops
Wispr Flow can adapt to a speaker's habits over time. It observes how your speech relates to the edits you make in the resulting text, then uses those corrections to improve its handling of your particular style.
This does not mean full model retraining. Instead, lightweight adaptation adjusts classification thresholds around your vocabulary and speech patterns. If you regularly use "right" as a genuine sentence ending rather than a filler, that preference receives more weight as the system learns from your corrections.
What You Should Expect in the Text
Wispr Flow's cleanup layer performs contextual language judgment, not mechanical word deletion. It will not be correct every time. If a document depends on exact wording, reviewing the result remains a sensible step.
For everyday prose such as emails, working notes, document drafts, and Slack messages, cleanup usually brings the transcript close enough to finished that your remaining edits concern the idea itself, not the sounds of thinking. That difference makes voice writing practical, rather than merely possible. A raw transcript that demands manual repair is a different experience from clean prose appearing as you speak. The NLP layer is what separates those two outcomes.