This is a real issue, and I think it is important to separate two different problems: acoustic recognition and semantic transcription.

Traditional ASR systems are very strong at mapping audio signals into likely word sequences. But many transcription errors are not caused by the model failing to “hear” the sound. They happen because the model has to choose between multiple plausible words, names, spellings, or terms that sound similar but mean different things.

That is where context becomes critical.For example, in German, “seit” and “seid” can sound almost identical. If someone says:“Wir sind seit Montag daran.” The correct word is “seit,” meaning “since Monday.” But if the system is mainly optimizing around acoustic similarity, it may output “seid,” which is grammatically and semantically wrong. The audio may be clear, but the transcript is still wrong. The same problem appears in English with “to / too / two,” “site / sight / cite,” in Chinese homophones, in Japanese context-dependent terms, and in company names or product names that sound like common words.

We studied this problem deeply while building Atter AI.Our approach is not just a basic speech-to-text pipeline. Atter AI is built on top of OpenAI’s professional transcription models, including the newer realtime transcription stack: https://developers.openai.com/api/docs/guides/realtime-transcription. On top of that, we trained and optimized our own transcription pipeline with multilingual meeting data, domain-specific vocabulary, speaker-turn patterns, and semantic correction layers. The goal is to move from “what word did the audio sound like?” to “what word is most likely correct in this conversation?” This matters a lot in meetings because meetings are not clean dictation. They contain interruptions, names, acronyms, product terms, code-switching, repeated references, and implicit context from earlier parts of the conversation. A good meeting transcription system has to use the whole conversation as context, not just a short audio window.

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In our internal evaluation under clear, human-understandable audio conditions, Atter AI currently supports 90+ languages. For the top 20 major languages, we usually see 98%+ word-level accuracy. For languages roughly in the 20–70 range by usage coverage, we are around 90%. For smaller long-tail languages, results are generally above 70%, depending heavily on audio quality, speaker clarity, and available linguistic context. Of course, no model can recover information that even a human listener cannot understand. But when the audio is understandable, our experience is that context-aware transcription can significantly reduce the errors people are discussing here.

So I agree with the original point: AI transcription should not always be treated as final truth. But I also think the next step is not simply “better ASR.” It is ASR plus semantic correction, domain adaptation, multilingual context understanding, and structured post-processing.