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.

I don’t think they take into account language context like completion models do. They just say words with limited memory. Hopefully research will unify them somehow.

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)

Ok I’ll admit to only having used neural models not trained them. AWS Polly is incredibly monotoned last I used it.

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.

Yeah I was using neural poly which is equivalent to wavenet. What I discovered is it will always say the same sentence, and usually the same word used in the same way, the same way, regardless of context clues. “My gosh.” Would always render exactly the same way. Really needs paragraph or dialogue driven context, as well as a bit of randomization. In a book where an author has a repetitive goto word or phrase it’s killer.

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.

Thanks for the reference I’ll look into it