No one will force a blind person to use a computer that converses in natural english. But even sighted people are likely to move away from dense visually heavy UIs towards natural conversational interface with digital systems. I suspect that given that comes to fruition (unlike us nerds, regular folks hate visual info dense clutter), young blind people won't even perceive much impediment in that area of life.
This isn't far off from CLI vs GUI debate, where CLIs are way faster and more efficient, but regular people overwhelmingly despise them and use GUIs. Ease over efficiency is the goal for them.
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.
There are effectively two approaches to voice synthesis: time-domain and pitch-domain.
In time-domain synthesis you care concatenating short waveforms together. These are variations of Overlap and Add: OLA [1], PSOLA [2], MBROLA [3], etc.
In pitch-domain synthesis, the analysis and synthesis happens in the pitch domain through the Fast Fourier Transform (visualized as a spectrogram [4]), often adjusted to the Mel scale [5] to better highlight the pitches and overtones. The TTS synthesizer is then generating these pitches and converting them back to the time domain.
The basic idea is to extract the formants (pitch bands for the fundamental frequency and overtones) and have models for these. Some techniques include:
1. Klatt formant synthesis [6]
2. Linear Predictive Coding (LPC) [7]
3. Hidden Markov Model (HMM) [8]
4. WaveGrad NN/ML [9]
[1] https://en.wikipedia.org/wiki/Overlap%E2%80%93add_method
[2] https://en.wikipedia.org/wiki/PSOLA -- Pitch-synchronous Overlap and Add
[3] https://en.wikipedia.org/wiki/MBROLA -- Multi-Band Resynthesis Overlap and Add
[4] https://en.wikipedia.org/wiki/Spectrogram
[5] https://en.wikipedia.org/wiki/Mel_scale
[6] https://en.wikipedia.org/wiki/Dennis_H._Klatt
[7] https://en.wikipedia.org/wiki/Linear_predictive_coding
[8] https://www.cs.cmu.edu/~awb/papers/ssw6/ssw6_294.pdf
[9] https://arxiv.org/abs/2009.00713 -- WaveGrad: Estimating Gradients for Waveform Generation
"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"