On Automatic Detection of Cognitive Decline

Abstract

Cognitive decline refers to the gradual loss of thinking abilities, including memory, attention, reasoning, and problem-solving. It can be a normal part of ageing or a symptom of conditions like dementia or Alzheimer’s disease when it significantly interferes with daily life. Early diagnosis is crucial, as timely intervention can slow progression and improve quality of life. Emerging approaches such as Digital Linguistic Biomarkers, subtle changes in speech and language patterns captured through digital tools, offer a promising, non-invasive way to detect early signs of cognitive decline before more obvious symptoms appear and perform massive population screening. In this position paper, we contend that the prevailing paradigm for the automatic detection of cognitive decline, primarily relying on classifiers that analyse subjects’ linguistic productions at a single point in time, is not the most effective approach. Instead, we advocate for a paradigm shift toward longitudinal analyses that track linguistic patterns over decades. To support this perspective, we present an experiment in which we compile and analyse a long-term corpus of spontaneous speech productions from well-known individuals, enabling insights into cognitive changes across extended time spans.

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