Integrating Acoustic, Prosodic, and Phonological Features for Automatic Alzheimer's Detection

Early and accurate diagnosis of Alzheimers Disease (AD) is critical for effective intervention. While previous studies have explored speech-based biomarkers for AD, this paper presents the first systematic investigation of acoustic, prosodic, and phonological speech features for detecting this neurocognitive disorder. Our study has two main objectives: (1) to assess the individual impact of AD on a wide range of speech features, and (2) to identify the most informative feature subsets using a combination of four feature ranking methods and seven machine learning classifiers. We conducted our experiments using the publicly available ADReSS Challenge dataset, allowing for direct comparison with prior speech-based approaches. Our analysis focused on continuous acoustic and prosodic measures as well as discrete phonological features, both independently and in combination. The best performance, with an F1-score of 0.89, was achieved using the optimal subset of acoustic features with ensemble learning and by integrating all three feature types, suggesting that combining continuous and categorical speech features offers complementary diagnostic value. This approach surpasses all previous speech-only methods in the ADReSS Challenge and comes close to the best reported overall result from the campaigns dataset, even without using lexical information. The results were further validated through positive outcomes on the Delaware corpus (focused on MCI) and a set of speeches from President Ronald Reagan (who was diagnosed with Alzheimers). These findings suggest that speech patterns, beyond just the content, are more indicative of Alzheimers disease than previously thought, underscoring the potential of multi-layered speech analysis for non-invasive AD detection.