  Textless self-supervised speech models have grown in capabilities in recent
years, but the nature of the linguistic information they encode has not yet
been thoroughly examined. We evaluate the extent to which these models' learned
representations align with basic representational distinctions made by humans,
focusing on a set of phonetic (low-level) and phonemic (more abstract)
contrasts instantiated in word-initial stops. We find that robust
representations of both phonetic and phonemic distinctions emerge in early
layers of these models' architectures, and are preserved in the principal
components of deeper layer representations. Our analyses suggest two sources
for this success: some can only be explained by the optimization of the models
on speech data, while some can be attributed to these models' high-dimensional
architectures. Our findings show that speech-trained HuBERT derives a low-noise
and low-dimensional subspace corresponding to abstract phonological
distinctions.
