I have this idea that refuses to leave me alone, but I don't really have the time and dedication to investigate it, so I'll just give this one away here:

I believe a lot of effort in reading stems from parsing: words can be ambiguous in sense ("ball" could mean a spherical object or a festive event; "work" could mean a result of working, or it could be the verb [they work], or it could be an imperative [work!], ...). So disambiguating sentences containing words of degeneracy G1, G2, G3, ... have a total disambiguation space of G1 x G2 x G2 x ... interpretations. Then there is also grammatical disambiguation.

There was the recent paper showing how transformers effectively provide each instance of a word with not just it's sense but also its grammatical position encoded as vectors, with the root at the origin, its children a unit distance away, and their children another unit distance away from their parent etc... This turned out to be essentially the Pythagorean theorem.

Now let's make another oversimplification: let's pretend the human ear hears volume logarithmically (or at least understands the logarithmic scale for intensity, very plausible since different resonators emit exponentially decaying sound with half-lives that depend on the properties of the object [transmission at the boundary conditions]). On top of that make the assumption that the discernible frequency bins form essentially a constant Q transform (i.e. equal temperament bins, a good but rough approximation, see Plomp and Leveldt). This insinuates we can present a vector as a list of decibels for each frequency bin. My choice for audio is mostly because it would take 0 effort for the listener to align the auditory sensors: even if we rotate or move our heads very fast, there will be no appreciable doppler effect to a speaker, let alone headphones. If we chose visual intensities or graphs, we would have to either look at the same point on the screen for prolonged periods of time, or alternatively shift the image in our brains.

So the idea would be to have a fraction of the frequency bins correspond to the sense vector, and the rest of the bins for the grammatical positioning vector. Then sequentially play the vectors in a sentence (or in any order really, since in theory the grammatical position is encoded in part of the spectrum).

We could create multiple choice questions by playing a sentence (senses and grammar positions), then repeating the sound of a word of choice's sense without grammar frequency bins, and have the user select the right word in a different sentence, to make sure we are recognizing the sense specifically.

Then "hear" pre-parsed texts, but scramble the word order to force the user to rely on the grammar vector component of each word.

Then, later, generate sentences that are ambiguous without grammar vectors: the unordered set of {cat, mouse, sees, the, the} "rendered to sound" twice with different grammar vectors: once with the meaning "the cat sees the mouse" and once with the meaning "the mouse sees the cat" and the user cas to connect the sentence audio to the right written sentence, to verify the user is interpreting the grammar vectors.

Who knows how fast we could be hearing text? Could we learn to hear text as fast as we can see and abstract visual scenes, if parsing is done for us by transformers? "A picture says a thousand words"