“How accurate is it?” is the first question people ask about an AI transcript, and the honest answer is “it depends on the episode”. A calm two-person interview recorded on good microphones comes out very differently from a four-person panel recorded over video calls. Any single percentage you read on a product page is an average over someone else’s audio.
So instead of quoting a number, this article gives you two things: what actually moves accuracy, and a 10-minute test that tells you the error rate for your episode.
What affects accuracy
| Factor | Why it matters | What helps |
|---|---|---|
| Audio quality | Background noise, echo and compression hide sounds the model needs | A quiet room, a close microphone, a recorded local track |
| Overlapping speech | Two people talking at once usually cannot both be written down | Ask guests not to interrupt; check those passages by ear |
| Names and jargon | Rare names, brands and technical terms are guessed from sound | Check each name against the episode page or the guest’s site |
| Accents and speaking speed | Less common accents and very fast speech raise the error rate | Set the right spoken language; review fast passages |
| Language mixing | A speaker who switches language mid-sentence confuses the language model | Expect extra errors at the switches |
| Numbers and dates | Digits and units are easy to mishear (“fifteen” and “fifty”) | Verify every figure you plan to use |
None of this is specific to one tool. It is how speech recognition works: the model turns sound into the most likely words, and unlikely words lose to likely ones.
The 10-minute spot-check
Accuracy for transcripts is usually reported as word error rate (WER):
WER = (substitutions + deletions + insertions) / words in the correct text- A substitution is a wrong word (“fifteen” for “fifty”).
- A deletion is a word that was spoken but is missing.
- An insertion is a word in the transcript that was never said.
A WER of 5% means about one wrong word in twenty. Lower is better. You can measure it on a sample without checking the whole episode.
- Pick three segments of about one minute each: one from the start, one from the middle, one from the end. Avoid ads and music.
- Listen and correct. For each segment, read the transcript while you listen and mark every substitution, deletion and insertion. Play it at normal speed; you will find most errors on the first pass.
- Count the words in the corrected segment. A minute of conversation is often 130 to 170 words.
- Add up the errors and divide by the word count. Do the same for all three segments, then average them.
- Note where the errors sit. If most come from names and numbers, the transcript is fine for reading and needs a quick edit before quoting.
Use a simple sheet:
| Segment | Words | Substitutions | Deletions | Insertions | WER |
|---|---|---|---|---|---|
| Start | 150 | ||||
| Middle | 150 | ||||
| End | 150 |
Three minutes of audio is a small sample, so treat the result as a guide, not a guarantee. Do the test on a second, harder episode before you rely on a tool.
Not all errors cost the same
A WER number treats every wrong word alike. Readers do not. A missing “the” hardly matters; a wrong name or a flipped “not” changes the meaning. When you check, give extra attention to:
- Names of people, companies and places
- Numbers, dates, prices and percentages
- Negations (“can” and “cannot”, “did” and “didn’t”)
- Quotes you plan to use word for word
Make checking fast
The slow part of verification is finding the right place in the audio. In Podcast To Transcript every transcript line has a timestamp and a speaker label, and a click on a line moves the player to that moment, so you can hear a doubtful passage in seconds instead of scrubbing through the episode. Pick the right spoken language before you transcribe, and read the transcript with the audio for names and figures.
If accuracy matters for your work, such as quotes in an article or a study, see the pages for journalists and researchers, and the guide to transcribing a podcast. To cite what you verified, read how to cite a podcast.
Summary
- Accuracy depends on your audio, not on a headline number.
- Measure it: three one-minute samples, count the errors, divide by the words.
- Always check names, numbers, negations and quotes by ear before you publish.
Frequently asked questions
How accurate is an AI podcast transcript?
For clear speech with one or two speakers, current tools are right on the great majority of words. Accuracy falls with background music, crosstalk, heavy accents and recordings made over a phone line. The only number that matters is the one you measure on your own episode, with the spot-check above.
What is word error rate?
Word error rate (WER) is the share of words that are wrong, missing or added compared with a correct reference. A WER of 5 % means about 5 mistakes in every 100 words. It counts all words equally, so it hides the fact that a wrong name costs more than a wrong “the”.
Why are names and numbers wrong most often?
Rare names, brand names and numbers are hard to predict from context, so the model has little to go on. These are also the words a reader trusts most, which is why the checklist above starts with them.
Can I improve accuracy before I transcribe?
Yes. Record each speaker on a separate mic when you can, keep music low under speech, and set the spoken language correctly instead of relying on detection. Better audio helps more than any setting.
Do I still need to proofread?
Yes, for anything public. Treat the AI text as a fast first draft, check names, numbers and quotes by ear, and fix them before you publish. Podcast To Transcript keeps timestamps next to each line, so you can jump to the exact second and listen. The pricing page lists what each plan includes, and the guide to transcribing a podcast walks through the steps.
