Automatic detection and sub-typing of primary progressive aphasia from speech: integrating task-specific features and spatio-semantic graphs

Primary progressive aphasia (PPA) describes a group of neurodegenerative diseases that predominantly affect language abilities. Its diagnostic process typically requires experienced clinicians, often available only in specialised hospital departments. Patients with PPA frequently display changes in speech and language early in the disease progression. In this study, we extracted acoustic, linguistic, and task-specific features from audio recordings and evaluated their utility for PPA classification. Using a subset of task-specific features, we detected PPA with 97% accuracy. For sub-typing, models trained on the full feature set achieved 74% accuracy in a three-way classification of PPA variants. Our results highlight the added value of task-specific features, which complement traditional approaches. Additionally, their visualisation offers an intuitive representation of task execution, improving clinical interpretability and potential diagnostic utility.