  Lying at the core of human intelligence, relational thinking is characterized
by initially relying on innumerable unconscious percepts pertaining to
relations between new sensory signals and prior knowledge, consequently
becoming a recognizable concept or object through coupling and transformation
of these percepts. Such mental processes are difficult to model in real-world
problems such as in conversational automatic speech recognition (ASR), as the
percepts (if they are modelled as graphs indicating relationships among
utterances) are supposed to be innumerable and not directly observable. In this
paper, we present a Bayesian nonparametric deep learning method called deep
graph random process (DGP) that can generate an infinite number of
probabilistic graphs representing percepts. We further provide a closed-form
solution for coupling and transformation of these percept graphs for acoustic
modeling. Our approach is able to successfully infer relations among utterances
without using any relational data during training. Experimental evaluations on
ASR tasks including CHiME-2 and CHiME-5 demonstrate the effectiveness and
benefits of our method.
