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

Alexandra König | Zendy; Nicklas Linz | Zendy; J. Tröger | Zendy; Maria Wolters | Zendy; Jan Alexandersson | Zendy; Phillipe H. Robert | Zendy

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

Author(s) -

Alexandra König,

Nicklas Linz,

J. Tröger,

Maria Wolters,

Jan Alexandersson,

Phillipe H. Robert

Publication year - 2018

Publication title -

dementia and geriatric cognitive disorders

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.026

H-Index - 110

eISSN - 1421-9824

pISSN - 1420-8008

DOI - 10.1159/000487852

Subject(s) - verbal fluency test , task (project management) , dementia , fluency , computer science , cognition , neurocognitive , cognitive impairment , natural language processing , artificial intelligence , cognitive psychology , speech recognition , psychology , neuropsychology , medicine , psychiatry , disease , mathematics education , management , pathology , economics

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.

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