Do speech foundation models really learn words?
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Computer Science > Computation and Language
arXiv:2609.10434 (cs)
[Submitted on 9 Sep 2026]
Title:Do speech foundation models really learn words?
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Abstract:Self-supervised speech foundation models are now used in a wide array of downstream applications, including traditional speech recognition and as the basis for tokens in speech-aware language models. Attempts to understand their usefulness have largely focused on probing their representations' ability to discriminate phonemes and words. However, discriminative ability for words need not imply specialized representation of words per se. Good discrimination of words may be explained by good encoding of word form (phonemes) rather than form-independent word representations encoding identity or syntactic/semantic properties. By partialling out phoneme information using residualization, we show that, in later layers, HuBERT and wav2vec 2.0 do in general learn representations which encode words with reasonable fidelity independently of local phonetic content. We show that this simple approach to disentanglement can enhance higher-order linguistic information in word discovery tasks.
| Comments: | Proceedings of Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2609.10434 [cs.CL] |
| (or arXiv:2609.10434v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.10434
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Do speech foundation models really learn words?, by Robin Huo and Ewan Dunbar
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