From Decodability to Reusable Features: Mandarin Lexical Tone in Speech SSL Models

Speech SSL representations can make a linguistic contrast decodable without organizing it into a stable, reusable feature. Mandarin lexical tone makes this distinction testable because T1–T4 form a closed inventory whose realization varies across speakers and syllables. We fit label-blind TopK sparse autoencoders (SAEs) to Mandarin HuBERT and wav2vec 2.0 states from 3,000 AISHELL-3 utterances. Selected features are tested on unseen initial–final types and across in- dependent dictionary fits. Full-state probes, rank-matched decoder-span and PCA controls, and interventions separately assess information availability, inventory re- covery and downstream influence. Tone is decodable at every evaluated layer, and many SAE features transfer and recur, but stable sparse localization is uneven and concentrated in T3. With 64 features, sparse SAE activations retain about 80% of the full four-tone probe gain, whereas the span of the same decoder directions retains 99–102%, matching the full-state endpoint and selected PCA directions. The selected SAE geometry supports the complete inventory, but the TopK ac- tivation map exposes it incompletely. SAE and PCA directions also influence a frozen downstream tone readout. The gap between decoder-span and TopK recov- ery supports a feature-absorption account in which tone evidence is distributed across context-conditioned activations rather than assigned to stable category co- ordinates. The study separates availability, factor-general localization, category coverage and downstream access.