The honest answer is that there's no single accuracy number that means anything. Any tool can quote you a figure, but a figure measured on clean read-aloud audio in major languages tells you almost nothing about how it will do on your call — with your accents, your jargon, your people talking over each other, and your languages. Real-world accuracy depends on conditions, and the only number that matters is how well a tool does on your meetings, in your languages. The good news: you can test that in a single call.
"Accuracy" isn't one number
There are at least two different things hiding behind the word. Transcription accuracy is whether the speech-to-text got the words right. Translation accuracy is whether the translated meaning is right — and a perfect transcription can still produce a wrong translation if word order, negation, or register flips. They fail in different ways and a single headline percentage blends them into mush.
It gets murkier. A model reading one clean, scripted sentence into a good microphone scores very differently from the same model in a real meeting — overlapping speech, background noise, someone on a phone, a name it's never seen. A high score on a vendor's benchmark and your actual Tuesday standup are not the same test. So a single headline figure, stripped of the conditions it was measured under, is close to meaningless.
What actually moves accuracy in a real meeting
In rough order of how much they hurt, the things that move accuracy on a real call:
- Audio quality — a cheap laptop mic, a noisy room, or a bad connection degrades transcription before translation even starts. Garbage in, garbage out.
- Crosstalk — two people talking at once is one of the hardest things for any system, because there's no clean signal to transcribe.
- Accents and speaking speed — a fast, heavily-accented speaker is harder than a slow, neutral one, in every language.
- Code-switching — mixing English into another language mid-sentence (Hinglish, Taglish, Singlish) breaks tools that assume one language per line.
- Domain jargon and names — your product names, acronyms, and people's names are exactly the words a general model hasn't seen, and exactly the ones you can't afford to get wrong.
- The specific language — the big one. Many tools were built English-first, and accuracy falls off a cliff on Asian and other under-resourced languages. (The Asian-language traps most tools fall into.)
Why the language matters more than the headline number
A tool can be genuinely good on English and the major European languages — and fall apart on Cantonese, Japanese, Tamil, Arabic, or Thai. A Mandarin model misreads Cantonese; eager captions flip Japanese and Korean negation; stripped Vietnamese diacritics change the word; Thai has no spaces to segment. One global accuracy figure hides all of this, because it's an average — and your meeting isn't the average. If your team works across Asian and under-resourced languages, the headline number is the least informative thing about how a tool will do, because it tells you nothing about the languages you actually use.
How to test accuracy on your own meeting
Don't trust a demo and don't trust a number — trust a native speaker on your content. Here's the honest five-step test:
- Run one real call with your actual languages and your actual people — not a scripted demo with clean audio.
- Have a native speaker read the captions and transcript for each language. They'll catch in thirty seconds what a spec sheet never tells you: does it sound like a person, or like a near-miss?
- Check the latency keeps up — captions roughly two seconds behind speech keep a discussion flowing; much more and people stop reading.
- Check code-switching stays whole — say a sentence that mixes English into another language and see whether both halves survive.
- Check the names, numbers, and jargon — your product names, dates, and figures are where small errors do the most damage.
While you're at it, check how the tool handles your data, since the same call answers both questions. (Does your meeting tool train AI on your conversations?)
How Sageio approaches it
We built Sageio Asian-language-first, because that's where most tools are weakest and where honest evaluation matters most. A bot joins your Google Meet, each participant reads their own language as live captions across 20+ languages in about two seconds, and within about five minutes of the call ending you get a searchable transcript and an AI summary. We don't publish a single accuracy percentage, because we don't think one would tell you anything true about your meeting. The right test is your own call — which is exactly why every plan starts with a free 60-minute trial. Put your real languages and your real people in front of it and let a native speaker judge.
Is it private?
For anything that joins your meetings: Sageio doesn't use your meeting content to train AI models, and its AI vendors are contractually restricted from doing the same. Audio is processed in memory and discarded — only the text transcript and summary are kept, encrypted, in the region you choose (US, EU, or APAC). Enterprise customers can self-host the entire stack.
Frequently asked questions
Is there a single accuracy percentage I can trust? No — and be wary of anyone who gives you one. A percentage is only meaningful with the conditions it was measured under: which languages, what audio quality, scripted or real speech. A number from clean read-aloud audio in major languages won't predict how a tool does on your real, multilingual, overlapping call. Test it on your own meeting instead.
What hurts accuracy the most? Audio quality and crosstalk degrade everything before translation even begins, so a good mic and people not talking over each other matter more than most people expect. After that: heavy accents and fast speech, code-switching mid-sentence, and unfamiliar names and jargon. And underneath it all, how well the model was built for your specific languages.
Are Asian languages less accurate? It depends entirely on the tool. Many were built English-first and do mishandle Asian languages in specific ways — but a tool built for them does not. That's exactly why you can't trust a global average and have to test on the languages your team actually uses, with a native speaker reading the output.
Does latency affect accuracy? They're separate but related. A tool can be slow and accurate, or fast and wrong. What you want is both — captions that keep pace (around two seconds) and read correctly. The own-meeting test checks both at once, which is the point of running it on a real call.
What does it cost to try? Every plan starts with a free 60-minute trial, no credit card required. After that, Professional is $49/month and Teams is $99 per seat/month (annual billing includes 2 months free); Enterprise is custom-priced.
Accuracy is real, but it isn't a number on a slide — it's whether a native speaker reads your meeting and recognizes it. No vendor figure can tell you that, including ours. So don't take the number; take the test. Add the bot to one real call, in your real languages, and let the people who'd notice the errors tell you whether they're there.