Multi-Class Dementia Detection Using Acoustic Features - ICASSP-2025 PROCESS Challenge

This paper describes our best-performing submission for the ICASSP-2025 Signal Processing Grand Challenge PROCESS, focused on the classification of speech into 3 groups - Healthy, Mild Cognitive Impairment (MCI), and Dementia - using three speech tasks in English. Our approach was aligned with the aim of simple, preclinical detection of dementia, employing a minimal set of acoustic features, and no linguistic analysis. We built an ensemble classifier based on 1) Selected features from the ComParE acoustic feature set and 2) knowledge-based rules for combining predictions across the 3 tasks, using a two-tier majority vote system. Our technique outperformed the baseline results by a large margin, achieving a macro-F1 of 0.96 on the development set, 0.99 on 5-fold cross-validation and 0.64 on the test set.