Exploring Gender Bias in Alzheimer's Disease Detection: Insights from Mandarin and Greek Speech Perception

TL;DR

This study uncovers gender bias in perceiving Alzheimer's speech, especially in Chinese, highlighting the influence of acoustic features and emphasizing the need to address gender bias in AD detection models.

Contribution

It reveals gender bias in AD speech perception across languages and identifies acoustic correlates, emphasizing the importance of mitigating gender bias in diagnostic models.

Findings

Male speech more often perceived as AD

Shimmer values linked to AD perception

Language did not significantly affect AD perception

Abstract

Gender bias has been widely observed in speech perception tasks, influenced by the fundamental voicing differences between genders. This study reveals a gender bias in the perception of Alzheimer's Disease (AD) speech. In a perception experiment involving 16 Chinese listeners evaluating both Chinese and Greek speech, we identified that male speech was more frequently identified as AD, with this bias being particularly pronounced in Chinese speech. Acoustic analysis showed that shimmer values in male speech were significantly associated with AD perception, while speech portion exhibited a significant negative correlation with AD identification. Although language did not have a significant impact on AD perception, our findings underscore the critical role of gender bias in AD speech perception. This work highlights the necessity of addressing gender bias when developing AD detection models and calls for further research to validate model performance across different linguistic contexts.