Integrating cognitive, linguistic and acoustic features to identify individuals with cognitive impairment: a proof-of-concept study

Early identification of cognitive impairment remains challenging in settings where comprehensive cognitive and clinical assessments are not available. Acoustic and linguistic features in naturalistic speech may serve as useful behavioural markers of cognitive impairment, but the value of integrating these measures with cognitive assessment remains unclear. We tested whether combining acoustic and linguistic features from one-minute speech samples with multi-domain cognitive assessment (spanning attention, language, memory and executive functions) improves classification of cognitively unimpaired individuals from those with amnestic mild cognitive impairment or early-stage Alzheimer's Disease. Across multiple machine learning models, combining cognitive, acoustic and linguistic features yielded significantly better classification performance than models using cognitive or speech features alone (area under the curve = 0.96-0.98, both comparisons p < .05). This proof-of-concept study reveals that integrating speech-based measures with cognitive testing may improve identification of cognitive impairment, supporting the development of accessible and scalable multimodal screening tools for primary care.