A lot of the methods I listed don't have anything to do with speech per se. They DO have something to do with how to use data to e.g. remove unwanted variabilit

A lot of the methods I listed don't have anything to do with speech per se. They DO have something to do with how to use data to e.g. remove unwanted variability or achieve better class separation. What makes them old methods is that they were developed in the context of using Gaussian mixture models for modelling Hidden Markov model state output probabilities. As such you could perhaps apply them in classifying birdsong, gunshots, or whatever else (with varying success, of course..). I have no doubt that these methods and their acronyms will become irrelevant (perhaps they already are), but I guess some of the basic underlying ideas about variability will re-emerge in the training regime of DNNs. Sure, the algorithms (and implementations) for training DNNs are the same, but these ideas are incorporated in the preparation and handling of training data (compare that with augmentation, like creating translated images etc.).