Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
Deep transformer models have been used to detect linguistic anomalies in
patient transcripts for early Alzheimer's disease (AD) screening. While
pre-trained neural language models (LMs) fine-tuned on AD transcripts perform
well, little research has explored the effects of the gender of the speakers
represented by these transcripts. This work addresses gender confounding in
dementia detection and proposes two methods: the $\textit{Extended Confounding
Filter}$ and the $\textit{Dual Filter}$, which isolate and ablate weights
associated with gender. We evaluate these methods on dementia datasets with
first-person narratives from patients with cognitive impairment and healthy
controls. Our results show transformer models tend to overfit to training data
distributions. Disrupting gender-related weights results in a deconfounded
dementia classifier, with the trade-off of slightly reduced dementia detection
performance.
