Short answer first: most AI note-taking tools are built and trained primarily for English, and it shows the moment you use them in another language. Transcription mangles technical terms, names, and word endings, and the summary inherits every mistake. If you study or work in a language other than English, language quality is the single most important criterion, not the length of the feature list.
This article explains why some languages are hard for AI, how to test a tool yourself in a few minutes, and what to look for before you commit.
Why do AI note tools struggle outside English?
AI notes depend on two steps, and both can fail. First, speech is turned into text (transcription). Then the text is turned into structured notes (summarisation). A tool can easily be good at one and weak at the other.
Non-English languages are harder for a few reasons:
- Less training data. There is far more English audio and text to train on than most other languages. Models see other languages less often and guess wrong more.
- Compound words and inflection. Many languages stack nouns together or change word endings heavily. Models trained mostly on English split or spell them incorrectly.
- Domain vocabulary. The words from your specific course or industry, plus local abbreviations, are exactly where weaker models fall apart.
- Accent and pace. A fast lecturer does not sound like the polished American podcasts many models are fine-tuned on.
The result is familiar: a transcription that looks almost right, but where every fifth technical term is wrong. And a note built on a flawed transcription carries all of those errors forward.
How do you test a tool in your language?
You do not have to trust the marketing. Run your own five-minute test:
- Record two or three minutes of a real lecture or meeting, ideally with domain terms.
- Run the same audio through two or three tools.
- Count the errors in the transcription: wrong technical terms, wrong names, sentences that make no sense.
- Read the summary. Does it capture the key points, or is it generic and shallow?
A tool that handles your language well gets the terminology right and produces a summary you can actually study from. A weak tool forces you to correct so much that you might as well have taken the notes yourself.
What to look for
When you compare AI note tools with language quality in mind, these are the points that matter:
- Real language support, not just "many languages". Plenty of tools list your language in a dropdown without being any good at it. Test it, do not take it on faith.
- Accuracy on domain vocabulary. Everyday conversation is easy. The hard part is your specific curriculum or industry.
- A usable summary. A transcription is raw material. What you actually want is a structured note with the important points pulled out.
- Data residency. If you record confidential meetings or sensitive study data, where your data is stored matters. Many large tools are US-based with no clear regional guarantee.
- What you can do afterwards. Can you turn notes into flashcards? Can you upload a PDF or slide deck and get the same treatment? That decides whether the tool merely archives or actually helps you learn.
The big international names like Otter.ai and Fathom are solid in English but noticeably weaker on non-English domain vocabulary. It is not that they are bad tools, but that they are optimised for one language and one market. If that language is not yours, that is exactly where it hurts.
Transcription is not the same as notes
An important distinction that often gets missed: a raw transcription is not a note. It is a word-for-word record, including repetitions, filler, and tangents. What you can actually study from is a structured note with headings and the key points highlighted. A good tool takes you all the way from audio to a usable note, not just to a wall of text. If you want to get more out of the note-taking itself, we have collected concrete methods in effective note-taking.
Where Notibo fits
Notibo treats language quality as a core feature, not an afterthought. It was built to handle less-resourced European languages well, so transcription holds up on domain vocabulary and academic speech far better than most international competitors, and the summary gives you structured notes rather than raw text. You can record a lecture directly, upload a PDF or PowerPoint and get notes and flashcards from it, and all data is processed within the EU. Flashcards can be reviewed with spaced repetition, so what you capture in class actually sticks by exam time. If you take notes in a language that most tools treat as an afterthought, it is worth testing Notibo on your own lecture and seeing the difference for yourself.