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Fully automatic speech-based analysis of the semantic verbal fluency task

Alexandra König; Nicklas Linz; Johannes Tröger; Maria Wolters; Jan Alexandersson; Phillipe Robert
In: Victoria Chan-Palay (Hrsg.). Dementia and Geriatric Cognitive Disorders (Dement Geriatr Cogn Disor), Vol. 45, No. 3-4, Pages 198-209, Karger, Basel, 2018.


Background: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valu- able information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Methods: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer’s or related de- mentias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manu- ally and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Results: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). Conclusion: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.