ParaGeo: Decomposing Paralinguistic Variation into a Shared Latent Geometry

Abstract:Speech delivery varies with both the requested paralinguistic attribute and the linguistic content. We introduce ParaGeo, a matched-content decomposition of paralinguistic variation in a frozen speech language model. Synthesized audio tokens are replayed with a fixed listening prompt; pooled key/value (K/V) representations are centered and projected into a shared low-dimensional space. Our GLM-4-Voice probe spans 80 requested controls from 12 benchmark families across eight sentences. With a globally fitted calibration basis, content-held-out centroid accuracy using this basis is 9.49% versus a 1.25% permutation baseline; same-label cross-content cosine similarity is 0.285 versus 0.017, and both conditional permutation tests yield p = 0.001. A separate ten-scenario, six-style probe reveals reproducible contrast directions across scenarios. Static, additive, and temporal interventions produce attribute-, layer-, and schedule-dependent response profiles. These results provide a shared coordinate representation for measuring paralinguistic structure and an empirical starting point for latent speech control. Code is available at this https URL.

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2610.03125 [cs.SD]

(or arXiv:2610.03125v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2610.03125 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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