Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task
Open accessAbstract
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.
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Neural-network word embedding methods such as word2vec and GloVe have shown better performance than LSA for semantic modeling when enough training data is available.
“In recent years, several new word embedding methods based on neural networks have gained popularity, such as word2vec [26] or GloVe [27], which have shown improved performance over LSA for semantic modeling when sufficient training data is available [84].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [84]
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- Authors
- König, A., Linz, N., Tröger, J., Wolters, M., Alexandersson, J., & Robert, P.
- Affiliations
- Université Côte d'Azur (FR)
- German Research Centre for Artificial Intelligence (DE)
- University of Edinburgh (GB)
- Venue
- Dementia and Geriatric Cognitive Disorders
- Publisher
- S. Karger AG
- Published
- 2018
- Volume
- 45
- Issue
- 3-4
- Pages
- 198-209
- ISSN
- 1420-8008 (ISSN-L)
- 1421-9824
- OpenAlex
- W2806825704
- Cited by
- 77
- References
- 53
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 11 · 2023: 17 · 2022: 10 · 2021: 11 · 2020: 3 · 2019: 9 · 2018: 4
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Locations
- Dementia and Geriatric Cognitive Disorders (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Alan Turing Institute (TU/B/000030)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- semantic fluency, automated speech analysis, mild cognitive impairment, Alzheimer's disease and related dementias, cognitive screening, speech annotation, cognitive decline, older adults
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Electronic Data Processing, Female, Humans, Male, Neuropsychological Tests, Semantics, Speech, Task Performance and Analysis, Verbal Behavior, Geriatric Assessment, Area Under Curve, Cognitive Dysfunction
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