Interesting. I actually came to the exact opposite impression after finishing all 7 volumes. I felt it was a poorly written book with mostly cartoonishly shallow characters, but with occasional sparks of brilliance and a few interesting and novel ideas and observations. However, on balance it was a disappointment.

Even reading Harold Bloom's essays didn't convince me that it's a great work. He was also mostly focusing on the few great ideas in the book, while glossing over the ridiculous plot twists and simply bad writing and editing.

Is this a workaround for the weird Cerebras chip architecture? Would mainstream users who train on GPU benefit?

I think what you are looking for is called "expressive TTS". There have been a ton of papers in the last couple of years on the topic. Many provide code.

I've had some success with simply preserving the hidden state of the network from one sentence to the next.

SSML may not be expressive enough for your application.

Most likely you are using the original Polly method, which is based on gluing together sounds of different phonemes. That produces monotone speech.

Try Google wavenet. It's available through google cloud api just like Polly.

There's a neural version of Polly, but I never tried it.

Having trained multiple TTS models, I disagree. It's actually quite impressive that prosody is quite accurate. Moreover, even homographs are surprisingly accurate (e.g. word "read" is pronounced with the correct tense if it can be deduced from the sentence)

I looked for it once years ago, but couldn't find any. I don't think it's needed anymore. Current TTS systems based on neural networks are really good at producing speech with the right intonation from just the text.

The new machine learning based translators don't really have a set of rules. They essentially learn probabilities of different word combinations. (e.g. https://ai.googleblog.com/2019/10/exploring-massively-multilingual.html), which we could argue should count as "understanding" (since Searle didn't define it clearly).

Why are we wasting time on this? Searle made a few subtle mistakes and played a few tricks.

1. He never defines what "understand" means. Without a clear definition, he can play rhetorical tricks to support his argument.
2. Is it really possible to translate from English to Chinese by just following a book of rules? Have you seen "old" machine translations that were basically following rules - it was trivial to tell machine translation from human translation.