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.).

Your prediction that DNNs will replace much of the pipeline is very interesting to me, but I hypothesize that you're at least partially wrong. I predict that DNNs will impact early stages of the pipeline which operate on continuously valued inputs, but I am skeptical that that DNNs will ultimately be the best solution for late discrete processing (e.g., decoding, language modeling). That DNNs ever perform well in discrete classification tasks just tells me we haven't spent enough time feature-engineering.

It's the ability of DNNs to replace feature engineering that makes them interesting. They have completely obsoleted feature engineering in object recognition in just a few short years. Have you seen the latest DNN results in translation and image captioning? I think DNNs are quickly going to surpass the state of the art in language modeling.