What it measures
Word error rate, or WER, is the most common way to score a speech recognition system. You take a human-checked "reference" transcript, compare it with what the system produced, and count the mistakes. A WER of 5% means roughly five errors per hundred reference words. Lower is better.
WER counts three kinds of error:
- Substitutions (S): a wrong word replaces the right one.
- Deletions (D): a word that was spoken is missing.
- Insertions (I): a word appears that nobody said.
The score is (S + D + I) divided by N, where N is the number of words in the reference. It is built on word-level edit distance, the minimum number of changes needed to turn one text into the other.
A quick example: if the reference is "please turn around" and the system writes "please burn a round," that is one substitution, one deletion and two insertions across three reference words, for a WER above 100%. Because insertions count, WER can exceed 100%.
What it hides
WER is useful and blunt. Every error weighs the same, so a dropped "the" costs as much as a mistaken drug name. It also depends heavily on the test set: clean studio audio, accents, noise, overlapping speakers and specialized vocabulary all move the number. Two vendors quoting different WERs may simply have tested on different audio.
How to read a WER claim
- Ask what test set and which languages were used.
- Check whether the text was normalized first, such as lowercasing and removing punctuation, since that changes the result.
- Be cautious when the claim comes from the company selling the model. Rerun a sample of your own audio.
- For voice agents, also watch latency and how errors affect the task, not only the score.
WER belongs to the family of AI benchmarks: a fixed test with a scoring rule. Transcripts feed voice agents, and tools such as evals let you measure accuracy on your own recordings. The opposite direction, turning text into audio, is text-to-speech.