Abstract

Abstract Alzheimer’s Dementia (AD) presents significant diagnostic challenges, particularly in terms of early detection, where traditional methods often fall short due to their invasiveness and high costs. This study introduces a novel, noninvasive approach utilising emotional expressions captured from audio recordings to detect AD. Employing advanced digital signal processing techniques, including Facebook’s Denoiser model, and deep learning methodologies through models such as Wav2Vec 2.0, this research aims to identify emotional disturbances that precede cognitive decline. Audio recordings were transformed into a tabular format, suitable for machine learning analysis. The LGBM Classifier and ensemble methods demonstrated superior performance, with the LGBM Classifier achieving the highest F1 score of 0.93 and an accuracy of 0.89 on a 3.5-second segment. These findings underscore the potential of combining emotional analysis with machine learning to enhance early AD detection, offering a simpler, more accessible diagnostic tool than currently available methods.