What meeting assistant actually works for non-English speakers

Let’s be blunt: the entire meeting assistant category is optimized for a room full of English-speaking, accent-free, corporate drones. Try deploying any of the usual suspects with a globally distributed team and watch the transcript turn into a surrealist poem.

I’ve been conducting an unofficial stress test across three platforms, including MeetGeek, with a team spanning Berlin, São Paulo, and Tokyo. The primary requirement was accurate transcription and actionable summaries for non-native English speakers with varying accents, and crucially, decent performance in accented English. The marketing claims rarely match the reality.

Here’s the dissection so far:

* **Transcription Accuracy:** This is the first and most brutal filter. For non-native speakers, even those fluent in English, most assistants fail on technical jargon and fast-paced, overlapping dialogue. MeetGeek’s handling of a German engineer’s heavily accented English was marginally better than some, but it still butchered key product names and mangled sentence structure to the point of changing meaning.
* **Language Support:** True multi-language support isn’t just about transcribing Spanish words. It’s about:
* Accurately capturing the meeting when participants code-switch.
* Providing summaries in a language different from the meeting’s spoken language.
* Handling proper nouns (names, local client projects) across languages. This is where most fall flat.
* **Analytics & Actionable Output:** If the transcript is garbage, the AI-generated "key takeaways" and "action items" are dangerous. A misattributed action item due to a transcription error creates more work, not less. I’ve seen a task for "review the API" become "review the app pie" – not helpful.

My core question for this subforum: Has anyone successfully implemented a meeting assistant (MeetGeek or otherwise) in a genuinely polyglot environment? I’m looking for concrete, attribution-ready data points:

* Specific language pairs and accent challenges you’ve tackled.
* How you validated transcript accuracy (manual spot-check methodology, error rate tracking).
* Whether the platform’s analytics (speaker talk time, sentiment, etc.) hold up with accented speech.
* Any workflow hacks or pre-meeting protocols you instituted to improve results.

I’m skeptical of any vendor case study that doesn’t provide a detailed breakdown of their linguistically diverse test group. The proof is in the poorly transcribed pudding.

--- M^2