I wonder how much better this would be at capturing information that doesn't translate well into text representations of speech. Consider how with word2vec the

I wonder how much better this would be at capturing information that doesn't translate well into text representations of speech. Consider how with word2vec there are relationships in the embedding space between semantically related words. I would expect the examples of that for word2vec (e.g. king -> queen being a similar translation as man -> woman) to apply here too, but can it also do things like place regular questions and rhetorical questions in different regions of the embedding space based off of of the inflection in the speech? It would also be interesting to see what relationships exist between equivalent words in different languages within the embedding space. I suppose something like that is probably already used for text translation neural networks, but maybe some notable differences exist when dealing with speech directly.