Self-supervised speech models can be trained to efficiently recognize spoken words in naturalistic, noisy environments. However, we do not understand the types of linguistic representations these models use to accomplish this task. To address this question, we study how S3M variants optimized for word recognition represent phonological and morphological phenomena in frequent English noun and verb inflections. We find that their representations exhibit a global linear geometry which can be used to link English nouns and verbs to their regular inflected forms.This geometric structure does not directly track phonological or morphological units. Instead, it tracks the regular distributional relationships linking many word pairs in the English lexicon—often, but not always, due to morphological inflection. These findings point to candidate representational strategies that may support human spoken word recognition, challenging the presumed necessity of distinct linguistic representations of phonology and morphology.

This paper summarises data collection and curation for Part 2 of the 2023 SIGMORPHON-UniMorph Shared Task 0, which focused on modeling speaker knowledge and generalization of a pair of interacting phonological processes in Korean. We briefly describe how modeling the generalization task could be of interest to researchers in both Natural Language Processing and linguistics, and then summarise the traditional description of the phonological processes that are at the center of the modeling challenge. We then describe the criteria we used to select and code cases of process application in two Korean speech corpora, which served as the primary learning data. We also report the technical details of the experiment we carried out that served as the primary test data.