How accurate is AI transcription?
Short answer
With a clear recording of one or two people, AI transcription gets the vast majority of words right; noise, echo, crosstalk, accents and names cause more errors. Accuracy is measured by word error rate. Always check names, numbers and negations against the audio before quoting.
With a clear recording of one or two people speaking normally, AI transcription gets the vast majority of words right. With noise, echo, heavy crosstalk or lots of names and jargon, it makes noticeably more mistakes. The honest answer to "how accurate is it?" is: it depends mostly on the audio.
Here's what drives accuracy, how it's measured, which mistakes show up most often, and a short checklist to run before you quote, publish or share a transcript.
What affects accuracy
- The recording: distance from the mic, background noise and echo matter more than anything else.
- Overlapping speech: when two people talk at once, short phrases get lost and lines can land on the wrong speaker.
- Accents and speaking style: a strong regional accent, mumbling or very fast speech raises the error rate.
- Names, brands and jargon: rare words are often replaced by common words that sound similar.
- Switching languages: a recording that alternates English and Spanish works, but accuracy can drop in the switched passages.
Word error rate, in plain words
Accuracy is usually measured with the word error rate (WER). You compare the transcript with a perfect reference and count three kinds of mistakes: wrong words (substitutions), missing words (deletions) and extra words (insertions). Add them up and divide by the number of words actually spoken.
So a WER of 5% means about 1 word in 20 is off, which on a page of 500 words is roughly 25 mistakes. That sounds like a lot, but many are small ("a" for "the") and don't change the meaning. A few are the kind that matter: a wrong number, a misspelled name, a dropped "not".
WER doesn't capture everything. It usually ignores punctuation and capitalization, and it doesn't measure whether each line was attributed to the right speaker. Treat any single accuracy percentage, from anyone, as a result for specific test audio, not a promise for yours.
Typical mistakes to look for
| Type | Example |
|---|---|
| Sound-alike words | "their" for "there", "except" for "accept" |
| Names and terms | "Priya" written as "Maria", a product name split into common words |
| Numbers | "fifteen" heard as "fifty", a wrong date or amount |
| Dropped short words | a missing "not" or "no" that reverses the meaning |
| Speaker attribution | a reply assigned to the person who asked the question |
Checklist before you quote or share
- Check every name, number, date and amount against the audio.
- Listen to any quote you'll use word for word; use the line's timestamp to jump straight to it.
- Look for negations (not, never, no) in important sentences.
- Confirm who said what in key exchanges, and rename speakers so the labels are clear.
- Read the noisiest or most heated parts closely; that's where errors cluster.
- For anything official, legal or medical, have a person verify the transcript. AI transcripts are not certified.
How Dolcenota helps you check
Every line has a timestamp, like [00:12:31] Speaker 1: …, so you can verify a passage in seconds. Rename a speaker once and the name updates everywhere; if one person got split into two labels, merge them (it's reversible). Corrections to the wording happen in your export: download Word, PDF or TXT and edit there. Each export carries a short note that it's AI-generated and may contain errors, which is the right expectation for any automatic transcript.
Frequently asked questions
Is AI transcription as accurate as a human?
On clear audio, it can be close. On noisy, overlapping or specialized recordings, a careful human transcriber still does better. Either way, check what matters.
What is a good word error rate?
Lower is better. A low single-digit WER means a transcript you can mostly read as is; higher rates mean more editing. The number always depends on the audio tested.
Why does it get names wrong?
Rare names sound like common words, and the system picks the more common option. Check names, and rename speakers in the transcript.
Does it handle Spanish and English in the same recording?
Yes, both are detected automatically, but accuracy can vary in the passages where speakers switch language.
Can I edit the transcript?
Export it to Word or TXT and edit it there. Speaker names can be changed in Dolcenota itself.
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