Transformer-based self-supervised speech models (S3Ms) are often described as contextualized, yet what this entails remains unclear. Here, we focus on how a single frame-level S3M representation can encode phones and their surrounding context. Prior work has shown that S3Ms represent phones compositionally; for example, phonological vectors such as voicing, bilabiality, and nasality vectors are superposed in the S3M representation of [m]. We extend this view by proposing that phonological information from a sequence of neighboring phones is also compositionally encoded in a single frame, such that vectors corresponding to previous, current, and next phones are superposed within a single frame-level representation. We show that this structure has several properties, including orthogonality between relative positions, and emergence of implicit phonetic boundaries. Together, our findings advance our understanding of context-dependent S3M representations.
@article{arxiv.2603.12642,
title = {Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces},
author = {Kwanghee Choi and Eunjung Yeo and Cheol Jun Cho and David R. Mortensen and David Harwath},
journal= {arXiv preprint arXiv:2603.12642},
year = {2026}
}