By way of example, an algorithm may very well be fed a more compact quantity of labeled speech data and then experienced on the much bigger list of unlabeled speech data to be able to produce a machine learning model effective at speech recognition.As an example, biased training data used for choosing conclusions may reinforce gender or racial ster