# Disentangling Acoustic Cues in Alzheimer's Pathology and Perception: The Roles of Language and Gender

arXiv: https://arxiv.org/abs/2607.23977 · HTML: https://mlsanity.com/paper/2607.23977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition](https://mlsanity.com/paper/2305.18011/) (2023)
- [Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models](https://mlsanity.com/paper/2409.16920/) (2024)
- [Exploring Local Interpretable Model-Agnostic Explanations for Speech Emotion Recognition with Distribution-Shift](https://mlsanity.com/paper/2504.05368/) (2025)
- [Exploring Gender Bias in Alzheimer's Disease Detection: Insights from Mandarin and Greek Speech Perception](https://mlsanity.com/paper/2507.12356/) (2025)

## Abstract (arXiv)

Acoustic biomarkers show promise for detecting Alzheimer's Disease (AD), yet whether the cues driving diagnostic AI align with those salient to human listeners is underexplored across languages and genders, where pathological markers and perceptual strategies differ. We train models to predict clinical AD status (pathology) and human perceptual scores across Mandarin and Greek, male and female speakers. Using SHAP for interpretability and statistical models for validation, we compare feature importance by subgroup. Results reveal a context-dependent divergence: pathological-perceptual alignment is significant for Mandarin and female speakers but disappears for Greek and male speakers, where pathology models did not exceed chance; this is a failure mode that population-specific auditing surfaces. Global Explainable AI (XAI) explanations can mask critical demographic divergences, highlighting the need for population-specific explainability auditing for equitable deployment of clinical speech AI.