A Comparison of Connected Speech Tasks for Detecting Early Alzheimer’s Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning
Open accessAbstract
Alzheimer’s disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited – 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) – and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech.
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Previous connected speech research has largely focused on Alzheimer disease, mild cognitive impairment, and PPA, with fewer studies focused on behavioral FTD variants.
“Previous connected speech research has largely focused on Alzheimer disease (AD), mild cognitive impairment, and PPA, with fewer studies focused on behavioral FTD variants.14-16”
From Automated Speech Analysis to Differentiate Frontal and Right Anterior Temporal Lobe Atrophy in Frontotemporal Dementia (2025) · cites this paper as [16]
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- Authors
- Clarke, N., Barrick, T. R., & Garrard, P.
- Affiliations
- St George's, University of London (GB)
- Corresponding author
- Clarke, Natasha
- Venue
- Frontiers in Computer Science
- Publisher
- Frontiers Media SA
- Published
- 2021-05-31
- Volume
- 3
- ISSN
- 2624-9898 (ISSN-L)
- OpenAlex
- W3168517739
- Cited by
- 65
- References
- 9
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 18 · 2023: 11 · 2022: 10
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Computer Science (publishedVersion, cc-by)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Medical Research Council
- 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
- Alzheimer's disease, mild cognitive impairment, early diagnosis, connected speech, speech analysis, picture description task, linguistic features, discourse features, map reading, support vector machine, speech biomarkers
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