The DOI matches its Crossref record.
Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People
Abstract
Background Various types of dementia and Mild Cognitive Impairment (MCI) are manifested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progress ion. Therefore, automatic speech analytics provided by a mobile application may be a useful tool in providing additional indicators for assessment and detection of early stage dementia and MCI. Method 165 participants (subjects with subjective cognitive impairment (SCI), MCI patients, Alzheimer's disease (AD) and mixed dementia (MD) patients) were recorded with a mobile application while performing several short vocal cognitive tasks during a regular consultation. These tasks included verbal fluency, picture description, counting down and a free speech task. The voice recordings were processed in two steps: in the first step, vocal markers were extracted using speech signal processing techniques; in the second, the vocal markers were tested to assess their 'power' to distinguish between SCI, MCI, AD and MD. The second step included training automatic classifiers for detecting MCI and AD, based on machine learning methods, and testing the detection accuracy. Results The fluency and free speech tasks obtain the highest accuracy rates of classifying AD vs. MD vs. MCI vs. SCI. Using the data, we demonstrated classification accuracy as follows: SCI vs. AD = 92% accuracy; SCI vs. MD = 92% accuracy; SCI vs. MCI = 86% accuracy and MCI vs. AD = 86%. Conclusions Our results…
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“Furthermore, researchers are actively investigating ways to assess cognitive impairment and dementia using mobile applications [123], [124] as well as conversational SAR acting as a psychologist [125].”
From Conversational Affective Social Robots for Ageing and Dementia Support (2022) · cites this paper as [123]
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Darmon, N., König, A., & Tröger, J. (2018) · French Journal of Psychiatry · doi:10.1016/s2590-2415(19)30181-3
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AI-powered assessments for mild cognitive impairment detection: A comprehensive review
Vinesh, T., Lavanya, M. S., Arun, V., Juremi, J., Tapkire, M. D., Shashikala, B. M., & Fatin, A. M. S. (2026) · AIP conference proceedings · doi:10.1063/5.0331506
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Metadata
- Authors
- Konig, A., Satt, A., Sorin, A., Hoory, R., Derreumaux, A., David, R., & Robert, P. H.
- Affiliations
- EA CoBTeK, University of Nice Sophia Antipolis, Nice, France
- IBM (United States) (US)
- Cognition Behaviour Technology (FR)
- Venue
- Current Alzheimer Research
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2018-01-03
- Volume
- 15
- Issue
- 2
- Pages
- 120-129
- ISSN
- 1567-2050 (ISSN-L)
- OpenAlex
- W2752744672
- Cited by
- 119
- References
- 0
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 18 · 2023: 18 · 2022: 14 · 2021: 22 · 2020: 10 · 2019: 8 · 2018: 3
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Alzheimer Research (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Eli Lilly and Company
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- speech analysis, mild cognitive impairment, dementia detection, Alzheimer's disease, mixed dementia, subjective cognitive impairment, older adults, mobile application, verbal fluency, picture description, automatic classification, early diagnosis
- MeSH terms
- Machine Learning, Aged, Aged, 80 and over, Dementia, Diagnosis, Computer-Assisted, Diagnosis, Differential, Female, Humans, Male, Neuropsychological Tests, Pattern Recognition, Automated, Speech, Cognitive Dysfunction, Mobile Applications
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