# Separating Voice from Age in COPD Screening

arXiv: https://arxiv.org/abs/2608.21599 · HTML: https://mlsanity.com/paper/2608.21599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs](https://mlsanity.com/paper/1705.07807/) (2017)
- [Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach](https://mlsanity.com/paper/2508.02354/) (2025)
- [Speaker-Disentangled Remote Speech Detection of Asthma and COPD Exacerbations](https://mlsanity.com/paper/2605.16878/) (2026)

## Abstract (arXiv)

Voice has been proposed as a low-cost screening signal for chronic obstructive pulmonary disease (COPD). COPD is strongly age-associated and voice changes with age, thus such results admit a trivial alternative explanation. We re-evaluate a public sustained-phonation corpus (1246 recordings, 68 participants) under a strictly participant-level protocol. We therefore evaluate on repeatedly drawn age-matched cohorts and report the discrimination achieved by the confounders themselves on those same cohorts. Where raw (unmodelled) age (0.510 [0.469, 0.551]) and raw gender (0.479) are both measured at chance, acoustic models excluding age retain ROC-AUC 0.717 [0.552, 0.859] and average precision 0.747 [0.581, 0.892] against a one-to-one baseline of 0.5, whereas models containing age fall to 0.531--0.679. The separation is reproduced by two further learners with fixed hyperparameters. Two findings have broader methodological implications: models trained with age transfer less effectively to an age-balanced target cohort than otherwise identical models trained without age, and fourteen classical voice-quality and perturbation measures achieve comparable discrimination to a 55-dimensional combined representation. We conclude that a non-age acoustic signal is present, that confounding by recording conditions cannot be excluded from the released features, and that the evaluation protocol in standard use cannot distinguish these possibilities.