From a computational linguist's point of view, is there a lower limit on the number of hours of speech needed to train a neural net to translate speech to text? An estimate from CMU is 3000-5000 hours for 90% accuracy commercial quality speech recognition. Is there is a minimum amount of information needed to reproduce the complexity of the actual language. I.e., if you had 5000 hours and compress it down with a neural net, there is some minimum size neural net needed to do a good job at speech recognition. You can call that the "bit complexity of a natural language". Does inverting the compression ratio tell you how many hours of speech, at a minimum, you would need to train a commercial quality speech recognition system? For context, the classic paper Prediction and Entropy of Printed English seems relevant.
Also relevant is the work of Sanjeev Arora on theoretical machine learning.
This question comes out of the NIST OpenCLIR19 evaluation and OpenSAT19. OpenSAT is having an open meeting in August. In both programs there is a focus on Low Resource Languages. OpenCLIR had 80 hours of Swahili and OpenSAT has Pashto. In OpenCLIR, no solver team was able to do speech recognition on Swahili training from 80 hours of speech. Hence the question.
