Word error rateWER
Word error rate (WER) is the standard measure of speech recognition accuracy: the number of words a model got wrong, divided by the number of words actually spoken. A WER of 5% means about one word in twenty was substituted, dropped, or invented.
WER counts three kinds of mistake. A substitution is the wrong word in the right place ("their" for "there"). A deletion is a spoken word that never appeared. An insertion is a word that appeared and was never spoken. Add them up, divide by the number of words in the correct transcript, and you have the rate. It can exceed 100% on bad audio, because insertions are unbounded.
The number is only meaningful next to the audio it was measured on. A model that scores 3% on clean, read English from a podcast microphone may score 15% on the same speaker in a café, and worse still on a language it saw less of in training. Published WER figures are almost always measured on standard benchmark sets, which are cleaner than your desk.
What WER does not capture
Every error counts the same, and they are not the same. A dropped "um" is an improvement; a dropped "not" reverses the meaning. A misspelled surname costs you a correction; a misspelled "the" costs you nothing because you never see it. Formatting is invisible to WER entirely: "1030 am" and "10:30 AM" are the same words. So a tool with a slightly worse raw WER and a much better formatting pass can produce text that needs far fewer fixes.
That is why the number you actually care about is closer to "corrections per hundred words after formatting", which no benchmark reports and which you can only measure by dictating your own work for a week.
How to read a vendor’s accuracy claim
Ask which benchmark, which language, and which microphone. "99% accurate" with no dataset named is a marketing sentence. Ask whether the figure is before or after any correction step, and whether a personal dictionary was in play. And weigh it against the things WER cannot see: names, numbers, and whether the text arrives as writing or as a transcript.
Improving your own error rate
A better microphone, closer to your mouth, helps more than any model change. Finishing sentences before releasing the key gives the model context to disambiguate. And a personal dictionary for the names and terms you actually say fixes the errors that repeat, which are the ones that matter, because a mistake that happens once a month is a nuisance and one that happens every day is a reason to stop dictating.
In Flit
We do not publish a WER for Flit, because the benchmark number would not describe your desk. We publish the things you can check instead: delivered speed, and what the text looks like when it lands.
What you said, what Flit wrote →Questions
Fair questions.
What is a good word error rate?
On clean English benchmark audio, the best current models score under 5%. In real use, with a laptop microphone and ordinary speech, 5 to 10% is typical and still very usable when the tool has a good formatting pass and a dictionary for your names.
How is word error rate calculated?
Substitutions plus deletions plus insertions, divided by the number of words in the correct transcript. A model that changes one word, drops one, and adds one in a hundred-word passage has a WER of 3%.
Is a lower WER always the better tool?
No. WER ignores formatting, punctuation, and which words were wrong. A tool that writes times, prices, and names correctly and removes fillers can need fewer corrections than one with a lower raw WER and no formatting pass.
