Just a variational autoencoder with a discrete latent space is good enough to learn a usable phoneme recognition and pronunciation model from raw WAV files with unsupervised learning.
And clip shows is just for far you can get without supervision. So what's the point in paying for artificial data if you can solve the problem without it?
It sounds like you are saying good synthetic data is more expensive?
With real world data a human will have to label it.
That's why so many AIs that work almost perfectly on the Sintel benchmark (synthetic flow and depth data) then fail to transfer over to KITTI (real images with lidar).
Even if you're doing completely unsupervised learning, you will still need correctly labeled data at some point to validate your model.
And - I tell you as someone who has actually tried to do this - getting a wide range of high-quality voice recordings across multiple speakers is NOT CHEAP, and trying to train a model that will work under real-world conditions using "whatever dataset I downloaded from kaggle.com" is a fool's errand.
Waiting for edge cases to occur, in the real world, is definitely not free.