Counting filler words when speech-to-text deletes them

We wanted a one-minute speaking test that gives a learner three numbers: words a minute, filler words and long pauses. The browser's speech recognizer would do the hard part. We'd count. It turns out the transcript fights you on all three. Lie 1: the model cleans up "um" Speech recognizers are trained to produce readable text, so "um" and "uh" mostly vanish. Count fillers from the transcript and a nervous speaker looks fluent. There's no fix for that from the page. What you can do