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Dement Geriatr Cogn Disord. 2018;45(3-4):198-209. doi: 10.1159/000487852. Epub 2018 Jun 8.

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

Author information

1
Memory Clinic, Association IA, CoBTek Lab, CHU Université Côte d'Azur, Nice, France.
2
German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany.
3
School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

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 dementias (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.

KEYWORDS:

Alzheimer’s disease; Assessment; Dementia; Machine learning; Mild cognitive impairment; Neuropsychology; Semantic verbal fluency; Speech processing; Speech recognition

PMID:
29886493
DOI:
10.1159/000487852
[Indexed for MEDLINE]

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