GeHirNet: A Gender-Aware Hierarchical Model for Voice Pathology Classification

• URL: http://arxiv.org/abs/2508.01172v1
• Date: Sat, 02 Aug 2025 03:19:44 GMT
• Title: GeHirNet: A Gender-Aware Hierarchical Model for Voice Pathology Classification
• Abstract summary: We propose a novel framework that first identifies gender-specific pathological patterns using ResNet-50 on Mel spectrograms, then performs gender-conditioned disease classification.<n>This work advances voice pathology classification while reducing gender bias through hierarchical modeling of vocal characteristics.
• Score: 4.504515306742906
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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