I like that you mentioned "inversion". ML is strongly related to solving inverse problems but it does not tend to be emphasised enough, in my opinion. In real

I like that you mentioned "inversion". ML is strongly related to solving inverse problems but it does not tend to be emphasised enough, in my opinion. In reality, speech organs (a relatively low dimensional system) generate high dimensional data (speech). This is an example of the so called curse of dimensionality. What ML does is to invert this process and create a model of the generation process. My favorite example is quantum mechanics. Physicists made lot of measurements and created a model (quantum mechanics) that reproduces those measurements (and even works on test data, not only on training data). This is an example of a solution of an inverse problem. ML does the same. So in one way or an other, every working TTS has a model that accurately describes the speech organs and how the brain uses them to create speech from text.