  Human listeners effortlessly compensate for phonological changes during
speech perception, often unconsciously inferring the intended sounds. For
example, listeners infer the underlying /n/ when hearing an utterance such as
"clea[m] pan", where [m] arises from place assimilation to the following labial
[p]. This article explores how the neural speech recognition model Wav2Vec2
perceives assimilated sounds, and identifies the linguistic knowledge that is
implemented by the model to compensate for assimilation during Automatic Speech
Recognition (ASR). Using psycholinguistic stimuli, we systematically analyze
how various linguistic context cues influence compensation patterns in the
model's output. Complementing these behavioral experiments, our probing
experiments indicate that the model shifts its interpretation of assimilated
sounds from their acoustic form to their underlying form in its final layers.
Finally, our causal intervention experiments suggest that the model relies on
minimal phonological context cues to accomplish this shift. These findings
represent a step towards better understanding the similarities and differences
in phonological processing between neural ASR models and humans.
