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

arXiv: https://arxiv.org/abs/2508.01172 · HTML: https://mlsanity.com/paper/2508.01172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020)
- [Classification of ALS patients based on acoustic analysis of sustained vowel phonations](https://mlsanity.com/paper/2012.07347/) (2020)
- [Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato](https://mlsanity.com/paper/2003.10806/) (2020)
- [Audio declipping performance enhancement via crossfading](https://mlsanity.com/paper/2104.03074/) (2021)

## Abstract (arXiv)

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.