A model of language could help with transcription, but having a perfect transcription doesn't mean you can make heads or tails of what you've got.

Noise and lack of specificity are completely different problems, and the article concerns itself with the latter

A noisy word might easily be guessed in context. But likewise a semantically ambiguous word might also be guessed due to other factors like the tone of the speaker, facial expressions or more.

I suspect the parent's point is that the disambiguation in both cases might be addressed with information encoded in the other. One can pattern match based on the context. I think some of the work in multimodal transformer models demonstrates this.

I think what you're saying is true, but I don't think this kind of ambiguity was really a feature of the examples in the article

I was replying specifically to the line in the comment I quoted, not the article as a whole.