-- speech to text
-- natural language understanding ( entity recognition, intent classification, entity resolution)
-- language generation and speech to text
It sounds like what they're saying is that DNNs are used for their speech recognition (speech is split into acoustic and language modeling, and DNN speech models have been state-of-the-art for about 4 years). It's kinda a "so what?" that they were using DNNs for their acoustic models, and nothing to do with the interesting parts of the system.
If they were using DNNs for their NLU system back then I'd be kinda surprised.
This is a great course, about NLP generally:
http://cs224d.stanford.edu/syllabus.html
A typical NLU system might be:
an intent classifier to predict intent (e.g., "play music"). This can be pretty much anything (SVM, logistic regression, RNN, I don't care, try stuff and see what works for you)
a segmentation model or slot-filling model (so label every word as either junk or "album name" or "song name" or whatever). This requires a sequence model, e.g., HMMs, CRFs, LSTM-RNNs.
Here's a nice example:
https://www.microsoft.com/en-us/research/wp-content/uploads/...