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

Alexandra König, Nicklas Linz, Johannes Tröger, Maria Wolters, Jan Alexandersson, Philippe Robert

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)198-209
Number of pages12
JournalDementia and Geriatric Cognitive Disorders
Volume45
Issue number3-4
Early online date8 Jun 2018
DOIs
Publication statusE-pub ahead of print - 8 Jun 2018

Keywords

  • Alzheimer’s disease
  • dementia
  • mild cognitive impairment
  • neuropsychology assessment
  • semantic verbal fluency
  • speech recognition
  • speech processing
  • machine learning

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