Language Orthogonalization of Self-Supervised Speech Representations for Cross-lingual Parkinson's Detection
TL;DR
This work proposes a closed-form ridge residualization of S3M features against external VoxLingua107 language embeddings, fitted using only healthy-control speech, which consistently improves cross-lingual PD-detection performance while correcting the high-specificity/low-sensitivity failure.
Abstract
Self-supervised speech models (S3Ms) provide powerful representations for Parkinson's disease (PD) detection, making cross-lingual transfer attractive for languages lacking labeled patient speech. However, these representations also encode language identity, which can confound this transfer: without target-language PD speech, classifiers may separate languages rather than pathology, yielding high specificity but low sensitivity on target patients. We propose \emph{language orthogonalization}, a closed-form ridge residualization of S3M features against external VoxLingua107 language embeddings, fitted using only healthy-control (HC) speech. By removing language-predictable components while retaining pathology-related variation, it produces a less language-dependent geometry in which HC representations concentrate while PD representations disperse. Across five S3M backbones, three speech tasks, and three target languages, our method consistently improves cross-lingual PD-detection performance while correcting the high-specificity/low-sensitivity failure.