Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection
Self-supervised learning (SSL) speech representations achieve strong performance for Parkinson's disease (PD) detection within individual corpora. However, it remains unclear whether these models capture disease-related characteristics or exploit dataset-specific confounds, particularly since most SSL backbones are pretrained exclusively on healthy speech. To investigate this question, we perform a layer-wise analysis of nine SSL speech backbones using a low-capacity logistic regression probe across three languages. We structure the evaluation as multiple scenarios that progressively introduce
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 45%Comparison of Large Language Model Performance on the United Kingdom Neurology Specialty Certificate Examination - Cureus →
- FuzzySimilar title/name (fuzzy) · 87%huggingface/speech-to-speech →
“Fuzzy title match (0.94): “Motor, Cognitive, or Corpus? What Survives Cross-Lingual Tra” ≈ “huggingface/speech-to-speech””
- LinkedLinked via arxiv author · 85%Serli Kopar →
“Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection”
- LinkedLinked via arxiv author · 85%Sam Gijsen →
“Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection”
- LinkedLinked via arxiv author · 85%Abner Hernandez →
“Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection”
- LinkedLinked via arxiv author · 85%Paula Andrea Perez-Toro →
“Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection”
- LinkedLinked via arxiv author · 85%Kerstin Ritter →
“Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection”