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

König, A., Linz, N., Tröger, J., Wolters, M., Alexandersson, J., & Robert, P. (2018). Fully automatic speech-based analysis of the semantic verbal fluency task. Dementia and geriatric cognitive disorders, 45(3-4), 198-209.


Background: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items 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 was 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 was an- notated 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 healthy controls from persons with Alzheimer’s (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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