What actually affects transcription accuracy
Accuracy percentages are quoted constantly and almost never defined. Here is what they mean.
· 7 min read
Every transcription product advertises an accuracy figure. Almost none of them say on what audio, which is roughly like advertising a fuel economy figure without saying whether it was measured downhill.
Understanding the underlying measure makes those claims much easier to read.
Word error rate, briefly
Accuracy in speech recognition is usually the inverse of word error rate: the proportion of words that were substituted, deleted or inserted compared with a correct reference transcript. A 10% word error rate means roughly one word in ten is wrong in some way.
Two things follow. First, "95% accurate" means one word in twenty is wrong — in a 5,000-word interview, that is 250 errors. Second, the figure depends entirely on the audio it was measured on, which is why the same system can honestly be described as 95% accurate and 80% accurate.
Audio quality dominates everything else
The gap between a close microphone in a quiet room and a distant one in a busy room is larger than the gap between competing transcription systems. If you are comparing tools, compare them on your audio, not on a vendor benchmark recorded under conditions you will never reproduce.
Accents matter less than they used to, and differently
Accent handling has improved substantially as training data has broadened, and accent is now a smaller factor than most people assume. What still causes trouble is the combination of an under-represented accent with poor audio — the two compound rather than add.
Code-switching, where a speaker moves between languages mid-sentence, is a separate and harder problem, and worth testing explicitly if it describes your recordings.
Jargon and proper nouns are the real weak point
A general-purpose system has seen "the patient presented with" thousands of times and your colleague's surname approximately never. Names, product names, technical terms and numbers are where errors concentrate — and they are also, inconveniently, the words that carry the most meaning.
Systems that let you supply context or a vocabulary list in advance can improve this measurably, because the problem is not really acoustic. It is that the system has no reason to expect the word.
Crosstalk breaks more than the words
When two people talk at once, accuracy drops for both — but the bigger casualty is often speaker attribution. A transcript that assigns the wrong sentence to the wrong person can be more damaging than one with a few misspellings, because the error is invisible in the text.
If accurate attribution matters to you, test with a recording where people interrupt each other, not a polite one.
How to evaluate a claim
When you see an accuracy number, ask three questions:
- On what audio was it measured — clean read speech, or realistic recordings?
- How many languages, and which? Multilingual averages hide wide variation
- Was it measured against a human reference transcript, and by whom?
A figure quoted with its benchmark is informative. A bare percentage is marketing.