You send a recording and get back text in the wrong language, a string of nonsense, or a sentence that was never said. Usually this isn't "bad AI" — it's a specific, fixable cause. Here's why transcription picks the wrong language and loses accuracy on accents and dialects, and what to do about it.
How to tell a transcript "broke" vs just slipped
A normal error is a couple of swapped words or a misspelled name — fine, you fix it by eye. These are signs the transcript failed outright:
- text in a different language, though the speaker used yours;
- words or phrases repeating in a loop;
- nonsense unrelated to what was said;
- "foreign" inserts — e.g. "subscribe to the channel" where no one said it.
That last one is a common artifact: the model has seen millions of clips ending that way and "completes" them when it loses its grip on the audio. If you see this, it isn't a few words — the engine misread the recording itself.
Why the model picks the wrong language
Modern transcribers auto-detect the language by default. On clean, long audio that works. But on short, noisy, accented, or mixed audio there's little signal to go on — and here a trait of today's models kicks in: they carry very strong language "priors." When the sound is ambiguous, the model may lean on its guess rather than what's actually there. For example, a short voice note with a heavy accent can make auto-detect "decide" it's Turkish and then confidently, fluently write in Turkish — instead of English.
The text looks coherent, which is what makes the error deceptive: it's not garbled noise, it's fluent speech, just in the wrong language. Two consequences follow — short clips transcribe worse than long ones (nothing for the model to latch onto), and recordings that mix languages are the hardest of all.
What actually improves accuracy
In order of impact:
- Set the language manually. The biggest lever. Take away the model's freedom to guess and tell it the language outright, and it stops sliding into the wrong one — this is what rescues accents, dialects, and low-quality audio. In TAK! TEXT you can set the recording's language manually on every plan — the first thing to do when auto-detect misfires.
- Quality vs Speed mode. Speed transcribes fast, but its language auto-detection is weaker and can miss on some languages. Quality is more accurate on noise, crosstalk, and heavy accents, and more reliable at detecting the language. If auto mode confuses the language — switch to Quality or set the language by hand.
- Audio. Less background noise and overlapping speech means higher accuracy. Clips longer than a few seconds transcribe more reliably than very short ones.
- One language at a time. If the recording mixes languages, set the dominant one so the model doesn't waver.
| Symptom | What to do |
|---|---|
| Text in the wrong language | Set the recording's language manually |
| Garble or looping repeats | Quality mode; check the audio |
| Poor on a dialect or accent | Fix the base language + Quality |
| Mixed languages | Specify the main language |
Dialects and accents are a special case
Auto-detection fails most on dialects: colloquial Arabic, regional variants, and strong accents knock the model off the base language. The fix is the same and especially noticeable here — fix the base language (e.g. Arabic) and switch on Quality mode. After that, dialect speech the auto mode turned into garble or another language transcribes fine.
Rare languages are their own story
Sometimes it isn't auto-detection but coverage. Many transcribers claim "90+ languages," but on Uzbek, Kazakh, or Armenian they return garble — because the underlying model doesn't really know those languages. Setting the language by hand won't save it: either the engine handles the language or it doesn't. Simple rule: with a rare language, run a short clip first — you'll see right away whether the tool can handle it before trusting a long recording. (Ours runs an engine that does handle these, but checking a short clip is still worth it.)
Don't expect post-processing to fix it
If the base transcript came out as nonsense, no amount of AI cleanup, summary, or translation will fix it — they work on top of the text, but the input is what needs fixing. So the order is: first get a clean base transcript (language, mode, audio), then summarize, translate, export. Trying to rescue a broken base with post-processing is wasted time.
Frequently asked questions
Why did the bot return text in a different language?
Most likely auto-detection kicked in and guessed wrong — common on short, noisy, or accented recordings. Set the recording's language manually and the text comes back in the right one.
Can I "fix" a broken transcript with AI processing?
Not if the base itself is broken. Summaries, translation, and cleanup work on top of the recognized text — garbage in stays garbage. Get a clean transcript first, then process.
What about dialects — like colloquial Arabic?
Fix the base language and switch on Quality mode. On dialects this gives the biggest gain: auto-detection fails on them most often.
Why do short voice notes transcribe worse than long ones?
The shorter the recording, the less context the model has to determine the language and meaning. On short clips, a manually set language helps most.