  Natural and artificial audition can in principle acquire different solutions
to a given problem. The constraints of the task, however, can nudge the
cognitive science and engineering of audition to qualitatively converge,
suggesting that a closer mutual examination would potentially enrich artificial
hearing systems and process models of the mind and brain. Speech recognition -
an area ripe for such exploration - is inherently robust in humans to a number
transformations at various spectrotemporal granularities. To what extent are
these robustness profiles accounted for by high-performing neural network
systems? We bring together experiments in speech recognition under a single
synthesis framework to evaluate state-of-the-art neural networks as
stimulus-computable, optimized observers. In a series of experiments, we (1)
clarify how influential speech manipulations in the literature relate to each
other and to natural speech, (2) show the granularities at which machines
exhibit out-of-distribution robustness, reproducing classical perceptual
phenomena in humans, (3) identify the specific conditions where model
predictions of human performance differ, and (4) demonstrate a crucial failure
of all artificial systems to perceptually recover where humans do, suggesting
alternative directions for theory and model building. These findings encourage
a tighter synergy between the cognitive science and engineering of audition.
