The state of modern AI text to speech systems for screen reader users

This almost perfectly encapsulates the problems that create friction for new technology. People want/expect the new technology to be an upgraded version of the old technology.

"AI is going to make screen readers amazing!"

No, that is not what AI is going to do. That is the exact kind of missing the forest for the trees that comes with new tech.

AI will be used to act as a sighted person sitting next to the blind person, who the blind person is conversing with (at whatever speed they wish) to interpret and do stuff on the screen. It's a total misapplication of AI to think the goal is to leverage it to make screen readers better.

They can have sighted servant who is gleefully collaborating with them to use their computer. You don't need 900 words per minute read to you so you can build a full mental model of every webpage. You can just say "Lets go on amazon and look for paper towels", "Lets check the top stories on HN"

This is missing large parts of the story.

Microsoft only bought the speech recognition / med tech parts of nuance, everything else, notably the Vocalizer speech stack (and likely also Eloquence) was spun off as Cerence. We know that somebody still has source code for Eloquence somewhere, as Apple licenses it and compiles it natively for aarch64 (yes I've looked at those dylibs, no there's no emulation). Not sure why nobody is recompiling the Windows versions, either there's just no need to do so, or some Windows specific part of the code was lost in all the mergers and would need to be rewritten.

A lot of Eloquence IP was also licensed by IBM, and the text-to-phoneme processing stuff is still in use for IBM Watson to some extend (it's vulnerable to the same crash strings and has similar pronunciation quirks).

With that said, I'm not sure if Eloquence system integrators are getting the Delta code and the tools to compile it to C++, or just the pre-generated cpp. Either would be consistent with the fact that Apple compiles it for their own platforms but doesn't introduce any changes to the pronunciation rules. It is entirely within the realms of possibility that this part of the stack has been lost, at least to Cerrence, though there's nothing that specifically indicates that such is the case.

Does having it sound "natural" even matter for high-speed reading? I assumed it would be a hindrance at higher speeds because natural variation and randomness in a voice makes it harder to scan the voice (similar to how reading something handwritten tends to be harder than something that has been typeset). At least that's how I always feel whenever I listen to audiobooks that use "natural" voices - I always switch to the more robotic sounding ones because, in my experience, it's easier to scan once at 2x and beyond.

My takeaway from the article is that accuracy of pronunciation, tweakability, and "time to first utterance" are what matter most.

With supertonic , or overall? If overall most do pretty well though some are funky, like suprano was so bad no matter what I did, so i had to rule that out from my top contenders on anything. supertonic was close to my number one choice for my agentic pipeline as it was soo insanely fast and quality was great, but it didnt have the other bells and whistles like some other models so i held that off for cpu only projects in the future. If you are gonna use it on a GPU I would suggest chatterbox or pocket tts. Chatterbox is my top contender as of now because it sounds amazing, has cloning and i got it down to 0.26 ttfa/ttsa once i quantized it and implemented pipecat in to it. pocket tts is probably my second choice for similar reasons.

I agree with you that someone who is good with a screen reader can efficiently move through web interfaces. A good screen reader user is faster than the typical user.

However, not all blind people are good with screen readers. For them, an AI assistant would be useful. Even for good screen reader users an AI could be useful.

An example: Yesterday, I needed to buy new valve caps for my car's tires. The screen reader path would be something like walmart -> jump to search field, type "valve cap car tire" and submit -> jump to results section -> iterate through a few results to make sure I'm getting the right thing at a good price -> go to the result I want -> checkout flow. Alternatively, the AI flow would be telling my AI assistant that I need new car tire valve caps. The assistant could then simultaneously search many provider options, select one based on criteria it inferred, and order it by itself.

The AI path, in other words, gets a better result (looking through more providers means it's likelier to find a better path, faster delivery, whatever) and also, much easier and faster. Of course, not only for screen reader users, but also just everyone.