{"ID":2887490,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01172","arxiv_id":"2508.01172","title":"GeHirNet: A Gender-Aware Hierarchical Model for Voice Pathology Classification","abstract":"AI-based voice analysis shows promise for disease diagnostics, but existing classifiers often fail to accurately identify specific pathologies because of gender-related acoustic variations and the scarcity of data for rare diseases. We propose a novel two-stage framework that first identifies gender-specific pathological patterns using ResNet-50 on Mel spectrograms, then performs gender-conditioned disease classification. We address class imbalance through multi-scale resampling and time warping augmentation. Evaluated on a merged dataset from four public repositories, our two-stage architecture with time warping achieves state-of-the-art performance (97.63\\% accuracy, 95.25\\% MCC), with a 5\\% MCC improvement over single-stage baseline. This work advances voice pathology classification while reducing gender bias through hierarchical modeling of vocal characteristics.","short_abstract":"AI-based voice analysis shows promise for disease diagnostics, but existing classifiers often fail to accurately identify specific pathologies because of gender-related acoustic variations and the scarcity of data for rare diseases. We propose a novel two-stage framework that first identifies gender-specific pathologic...","url_abs":"https://arxiv.org/abs/2508.01172","url_pdf":"https://arxiv.org/pdf/2508.01172v1","authors":"[\"Fan Wu\",\"Kaicheng Zhao\",\"Elgar Fleisch\",\"Filipe Barata\"]","published":"2025-08-02T03:19:44Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"eess.AS\"]","methods":"[]","has_code":false}