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Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task

Abstract : 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 valuable 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 manually 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.
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https://hal.inria.fr/hal-01850408
Contributor : Alexandra König <>
Submitted on : Monday, July 30, 2018 - 11:35:29 AM
Last modification on : Wednesday, October 14, 2020 - 4:25:07 AM
Long-term archiving on: : Wednesday, October 31, 2018 - 12:38:00 PM

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Alexandra König, Nicklas Linz, Johannes Tröger, Maria Wolters, Jan Alexandersson, et al.. Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task. Dementia and Geriatric Cognitive Disorders, Karger, 2018, 45 (3-4), pp.198 - 209. ⟨10.1159/000487852⟩. ⟨hal-01850408⟩

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