{
  "abstract": "Parkinsons disease (PD) affects speech production, motivating classification from repeated voice measurements. Record-wise evaluation can place recordings from the same participant in both training and test sets. We propose VoxNeuro, a novel subject-level framework combining Grassmann representations of repeated recordings with Euclidean mean-dispersion summaries through a provably positive-semidefinite kernel for support vector classification. Features undergo training-fold Gaussian rank normalization, and a supervised partial least squares subspace ensemble is activated when the feature count exceeds the number of training recordings. Two public cohorts were evaluated, UCI-489 (80 subjects) and PD-252 (252 subjects), each with three sustained-vowel recordings per subject. At the reference five-fold subject-disjoint partition, balanced accuracy was 87.5% and 81.6%, respectively, exceeding all five standard comparators under identical folds and normalization. PD-252 sensitivity was 89.9% and specificity was 73.4%. Across 20 additional subject partitions, PD-252 balanced accuracy averaged 78.4%, with a mean paired advantage of 3.2 percentage points over the highest-scoring standard comparator. VoxNeuro exceeded each standard comparator in at least 19 partitions for both balanced accuracy and macro-F1. Ablations favored rank normalization and the unaugmented supervised-subspace configuration on PD-252. VoxNeuro thus offers an effective and reproducible framework for subject-level analysis of repeated speech measurements in PD screening research.",
  "abstract_shown": true,
  "affiliations": [
    "Voxneuro"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "L. Khan"
    },
    {
      "name": "M. Waghe"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8c767ekme83a7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.27.26364114",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.27.26364114v1.full",
  "id": "8c767ekme83a7",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.27.26364114v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "neurology"
    ],
    [
      "openalex",
      "2737|Voice and Speech Disorders"
    ]
  ],
  "openalex": "W7214631906",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.27.26364114v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8c767ekme83a7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:35Z",
      "native_id": "10.64898/2026.09.27.26364114",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.27.26364114v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:neurology",
      "subfield": "neurology"
    }
  ],
  "title": "VoxNeuro: Leakage-Safe Parkinson Screening Support via Multi-View Grassmannian Speech Analysis",
  "title_text": "VoxNeuro: Leakage-Safe Parkinson Screening Support via Multi-View Grassmannian Speech Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:35Z",
  "url": "https://research.draconitic.com/paper/8c767ekme83a7",
  "venue": "medRxiv",
  "versions": []
}