Multilingual approaches for early Alzheimer's disease detection through analysis of spontaneous speech

Alzheimer's disease (AD) is a progressive neurodegenerative disease which has become a prominent global issue. Linguistic measures elicited from speech can provide important insights into cognition and prove useful in predicting cognitive decline in the early stages of AD. There is growing interest in using speech data for early detection based on computational linguistics methods. While various models have been proposed and applied in different language systems, progress has been hampered by (a) scarcity of speech datasets from clinical and preclinical AD patients; (b) lack of systematic multilingual studies aimed at identifying linguistic markers of AD that generalise across languages. This project will tackle these issues by developing dialogue protocols for collecting data from spontaneous speech conversations, and systematically comparing linguistic features derived from English and Chinese the detection of AD. Specifically, it will investigate network analysis methods that have been applied to English but not yet to Chinese data.