Exploring Language-Agnostic Speech Representations using Domain Knowledge for Detecting Alzheimer's Dementia

We explore ways to use speech data as a tool to screen for indications of Alzheimer’s dementia (AD). In particular, we describe our approach to the ICASSP 2023 Signal Processing Grand Challenge, which involves extrapolating from learning models from English speech samples, to Greek speech samples, to determine which subjects have AD. By using coustic and linguistic features, inspired by clinical research on AD, our top-performing classification model achieves 69% accuracy in distinguishing AD patients from healthy controls, and our regression model attains an RMSE of 4.8 for inferring cognitive testing scores. These outcomes underscore the potential of our explainable model for detecting cognitive decline in AD patients via speech, and its applicability in clinical settings.