Where codecs draw the line: allocation profiles for speech tokenisers

Quantising a speech representation is dividing it: a codebook partitions latent space, and every distinction finer than a cell is destroyed. Analyses of speech tokenisers ask what their codes retain, not where their boundaries fall. We charac- terise a partition by the reconstruction error each phonetic category incurs — an allocation profile, comparable where the partitions themselves are not. It separates two things a single test conflates: added levels may narrow the gaps between cate- gories, or change which ones are finely resolved. Across four widely used codecs and one we train to reverse a single architectural choice, depth does the first, and only quantisation in a learnt low-dimensional projection does the second. And what the first level resolves finely is largely a restatement of the signal: energy, duration and frequency account for 82.2% of one codec’s acoustic profile against 16.2% of the distilled stream trained beside it, which allocates along unrelated lines. Depth mostly refines an allocation that architecture has already settled.