Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces

Kwanghee Choi; Eunjung Yeo; Cheol Jun Cho; David R. Mortensen; David Harwath

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arXiv · 2603.12642

Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces

Kwanghee Choi·Eunjung Yeo·Cheol Jun Cho·David R. Mortensen·David Harwath

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Abstract

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.

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Kwanghee Choi, Eunjung Yeo, Cheol Jun Cho, David R. Mortensen, David Harwath. 2026-03-13. Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces. https://arxiv.org/abs/2603.12642

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