Accents and other languages
Interview transcription for accents and other languages
Name the spoken language on each recording, keep speaker labels through it, and correct the passages you are going to quote.
P04 · Cardiff
How did the ward respond when you raised it?
They took it seriously, mind, but nothing shifted for months.
EscalationSo I went round it in the end, through the union.
Open the sample workspaceNo account needed.
Name the language before it gets guessed
Automatic language detection listens to the audio and picks a language for you. Its own documentation records that heavily accented speech is sometimes identified as a different language, and that when detection lands somewhere unexpected, the features that language does not support are dropped from the result without an error. Speaker labels are one of those features.
So the language is a setting on the upload. Choose it once per recording and that interview is transcribed as Welsh-accented English, or as Portuguese, because you said so.
How a whole study moves through itI'm Welsh and transcription software hates me. Zoom, Teams, Otter. And I'm used to speaking slowly and clearly so that people can understand me.
Researcher, r/academia
Copy with citationWhat to expect in your language
Banded by the transcription engine's published word error rates, so you can decide before you spend an hour of allowance.
Most interviews need light correction on names and overlapping turns.
English, Spanish, French, German, Italian, Portuguese, Dutch, Catalan, Polish, Russian, Ukrainian, Swedish, Turkish, Indonesian, Japanese
Navigable straight away, and worth budgeting cleanup time before you quote.
Arabic, Mandarin, Korean, Hindi, Danish, Finnish, Norwegian, Czech, Croatian, Hungarian, Greek, Tagalog
Error rates here run high enough that retyping is faster than fixing. Import a transcript instead and keep the archive, search, and export.
Swahili, Yoruba, Bengali, Gujarati, Telugu, Hausa, Amharic
Bands reflect the transcription engine's measurements as of August 2026 and move as models change.
What holds when the wording does not
Set the language per recording
Choose it on the upload and that recording is transcribed as that language. A project can hold interviews in several, one setting each.
Speaker labels come through
Turns stay separated and named across the languages above, so you can navigate an interview whose wording still needs work.
Correct the lines you quote
Fix a word, split a turn, reassign a speaker. Correcting a whole transcript is wasted effort when three passages are going in the paper.
Transcript quality decides whose interview survives
When one participant's transcript comes back as nonsense because of an accent, the choice is to drop that interview or to analyse a version of what they said. Both answers change the finding, and neither is visible in the write-up.
That makes the language setting a data-integrity decision. Correcting in place leaves the recording as the record and the transcript as something you can defend line by line.
Where recordings are processed and storedOne of the interviewees had an accent that the AI didn't understand, so the transcript is nonsense. Do we just disregard that data? What implications does that have for bias and equity in our analysis?
Martina Donkers, evaluation and qualitative researcher
Copy with citationMetered by audio hour, in any language
An hour of Portuguese costs what an hour of English costs.
Free
Enough to see a corpus take shape.
$0forever
3 audio hours, once
- Unlimited projects, interviews, and tags
- Full-text search across every transcript
- Markdown, CSV, and REFI-QDA export
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A study's worth of interviews, every month.
$12per month
20 audio hours a month
- Hours reset on your renewal date
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RecommendedFor a fieldwork season or a whole cohort.
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50 audio hours a month
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Common questions about languages
- My participants have strong regional accents. Will this work?
- Set the language on the recording and it stays in that language, which removes the most common failure, where heavily accented speech is identified as another language and transcribed against the wrong model. The transcription engine documents that risk for automatic detection. Wording still needs correction on a strong accent, and speaker separation and timestamps hold, so you can find the turn and hear it.
- Which languages is it worth using for?
- The three bands on this page are drawn from the transcription engine's published word error rates. Fifteen languages sit in the cleanest band, twelve more are usable with extra correction time, and the ones listed at the bottom are not yet worth spending your hours on.
- What happens when an interview switches between two languages?
- Pick the language that carries most of the interview. Passages in the other one come back rough and stay findable by timestamp, so they can be corrected in place. Word-level switching between two languages through a whole interview is not something to buy this for.
- Do I get an English translation?
- No. Transcripts come back in the language that was spoken, which is the analysis object most methods guidance asks you to stay in. Translation stays your decision, made on the passages you quote.
- Do speaker labels work outside English?
- Yes, across the languages in the first two bands. Accuracy improves the more each speaker talks, so a two-person interview separates more reliably than a group where turns overlap.
- How accurate is it?
- The measure worth using is how much correction a transcript needs before you can quote from it, which depends on the language, the accent, the microphone, and how much people talk over each other. Three audio hours are free, so run one real interview through it and read the result before you decide.
- Where is the audio processed?
- In the European Union. The transcription service deletes its copy once the transcript lands, and nothing is used to train models. The data governance statement names the subprocessors and the retention.
Related pages
Run one accented interview through it
Three audio hours are free, which is enough to read a result in your own material before you commit.