22 August 2026
Accent alone almost never breaks a transcript from a model trained on enough varied audio. What breaks it is a sentence that starts in English and finishes in Tagalog, or a name that has no entry in any language model's vocabulary — and both failures look identical on the page: confident, grammatical, wrong.
Accent alone rarely breaks a transcript from a model trained on enough varied audio. What breaks it is the moment a speaker crosses from one language into another inside a single clause — 'so the vendor said, pwede ba bukas, let's just push it' — because the segmenter has already locked in a language before the switch happens acoustically.
Most speech models cut audio into segments of a few hundred milliseconds to a couple of seconds and assign each one a language before transcribing it. A clean sentence in one language, however heavily accented, fits inside that window. A sentence that switches languages mid-clause does not, and the tail gets forced through the wrong model — producing a real word, just not the one that was said.
A misheard proper noun rarely comes out as gibberish. The model substitutes the nearest word it actually knows, so a supplier's name becomes a common noun that sounds similar in the transcript's dominant language, and it reads as a normal sentence. Nobody scanning the page notices anything wrong, because nothing about it looks wrong.
This is worse for names than for ordinary vocabulary, because names carry no grammatical context to catch the error. A verb the model gets wrong usually breaks the sentence around it; a name it gets wrong just sits there, spelled with confidence.
Three things move the needle, in order of how much control a team has over them.
Running a translation pass on top of an already wrong transcript compounds the error instead of fixing it — translation has no way to know the source word was wrong, so it translates the mistake fluently into the second language too.
Judging accuracy by how clean the output reads is the opposite of useful here. A model unsure about a code-switch often produces smoother, more grammatical text than the messier thing actually said, because it fills gaps with its best guess at fluent language rather than transcribing the disfluency.
AVAY transcribes the call as it happens inside the browser, and a team can attach its own glossary of names and terms through a connector rather than correcting the same misheard supplier name every week. That fixes the recurring case.
It doesn't fix the one-off case: a sentence that switches languages mid-clause carries the same seam risk any speech model has, because the failure sits in how audio gets segmented before it's transcribed, not in which product does the transcribing. For a decision recorded in a mixed-language call, a bilingual read of the disputed line is still worth thirty seconds.
| Catches mid-sentence switches | Catches unfamiliar names | Setup effort | |
|---|---|---|---|
| Single multilingual model (auto-detect) | Rarely — commits to one language per segment | No — guesses the nearest known word | None |
| Per-speaker language lock | Better for two full languages, still misses a switch mid-clause | No | Low — set once per participant |
| Custom vocabulary or glossary | No effect on switching itself | Yes, for names added in advance | Medium — someone maintains the list |
| Bilingual spot-check after the call | Catches it after the fact | Catches it after the fact | High — a person's time every call |
Most can't cleanly. The segmenter usually commits to a language before the switch happens acoustically, so the second half of the sentence gets forced through the wrong model and comes out as a real but incorrect word. Tighter segment windows help a little; they don't fix a switch inside a few syllables.
Yes, for that specific name, and only if it's added before the call happens. It does nothing for a name the model hears for the first time mid-meeting, because there's no vocabulary entry to match it to.
Check the confidence flags most transcripts include — errors cluster there, not evenly across the page. Cross-reference any flagged name against a roster or CRM record, and ask a bilingual colleague to paraphrase the disputed line rather than retranslate it word for word.
Less than most people assume, for a model trained on broad audio data. The bigger driver is how much training data exists for that specific accent and how clean the input audio is — accent is a proxy for both, not the real cause.
AVAY transcribes what's said in the browser as the call happens, and a team can attach its own glossary of names and terms through a connector so recurring names stop getting mangled. A sentence that switches languages mid-clause still carries the same seam risk any speech model has, so a bilingual read of anything decision-critical is worth doing.
Accent isn't the failure point — the seam where two languages meet inside one sentence is, along with any name the model has never heard. Check confidence flags and get a bilingual paraphrase, not a polished read of the output.
AVAY is a video meeting platform that transcribes the call itself — no bot joins, because there is nothing to join. Start one at avay.ai, read how each part works in the documentation, or see what it costs.