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> Then again, that's probably easier to work around by having humans annotate the sentences to be read.

Or by starting with a recording of someone else reading the sentence. Then you get the research problem known as "voice conversion", which has been studied a fair amount, but mostly prior to the deep learning era - and mostly without the constraint of limited access to the target person's voice. (On the other hand, research often goes after 'hard' conversions like male-to-female, whereas if your goal is forgery, you can probably find someone with a similar voice to record the input.)

Anyway, here's an interesting thing from 2016, a contest to produce the best voice conversion algorithm, with 17 entrants:


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