Do speech foundation models really learn words?
HuBERT and wav2vec 2.0 appear to encode word identity beyond local phonetics in later layers. The paper offers a cleaner probe for builders evaluating speech representations.
The authors use **residualization** to remove phoneme information before probing speech representations. In **later layers**, HuBERT and wav2vec 2.0 still encode words with reasonable fidelity, suggesting their word discrimination is not solely phonetic.
Builders evaluating speech encoders should test what remains after obvious low-level signals are controlled for. The same disentangling approach may also make higher-order linguistic information more useful for **word discovery**.
The authors use **residualization** to remove phoneme information before probing speech representations. In **later layers**, HuBERT and wav2vec 2.0 still encode words with reasonable fidelity, suggesting their word discrimination is not solely phonetic. Builders evaluating speech encoders should test what remains after obvious low-level signals are controlled for. The same disentangling approach may also make higher-order linguistic information more useful for **word discovery**. The material reports the general finding but provides no effect sizes, dataset details, or model comparison numbers. It therefore supports a probing method and qualitative conclusion, not a production recommendation.
This strengthens benchmark-integrity practice for representation probes: apparent word knowledge should be tested after removing an obvious phonetic shortcut. The finding suggests later speech layers retain higher-order lexical information, but mainly confirms a disentangling method; absent effect sizes and dataset details, it cannot support encoder selection or establish how useful that information is downstream.