Multi-Task Learning with Acoustic Features for Alzheimer’s Disease Detection

This study explores the potential of acoustic features extracted from speech recordings for detecting Alzheimer’s Dementia (AD), employing a comprehensive approach that incorporates binary classification (healthy control vs. dementia), multiclass classification (healthy control, mild cognitive impairment, AD), and regression analyses (predicting MMSE scores). Additionally, demographic information of the participants was integrated to enhance the models’ predictive accuracy. Our methodology involved processing each dataset version through a series of machine learning models tailored to each task, starting with a baseline version, followed by hyperparameter optimisation, and finally applying a combination of preprocessing steps (scaling, outlier removal, dimensionality reduction, and skewness correction) to identify the optimal setup for each model.The findings indicate that preprocessing steps significantly improve model performance across all tasks, underscoring the importance of data preparation in machine learning workflows for healthcare applications. Notably, the use of acoustic data alone for AD detection shows promising results, suggesting a pathway toward more generalised approaches that could incorporate recordings in various languages without linguistic dependency. This opens up the possibility for scalable, non-invasive screening tools for AD, leveraging the universal nature of acoustic markers in speech for early detection and monitoring of this condition.