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Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People
Konig A, Satt A, Sorin A, Hoory R, Derreumaux A, David R, Robert PH.. Current Alzheimer research, 2018-01-01
Topic: Risk, prevention and care in population studies
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 indicate the potential value of vocal analytics and the use of a mobile application for accurate automatic differentiation between SCI, MCI and AD. This tool can provide the clinician with meaningful information for assessment and monitoring of people with MCI and AD based on a non-invasive, simple and low-cost method.
Key findings
- Results The fluency and free speech tasks obtain the highest accuracy rates of classifying AD vs.
- Using the data, we demonstrated classification accuracy as follows: SCI vs.
- Conclusions Our results indicate the potential value of vocal analytics and the use of a mobile application for accurate automatic differentiation between SCI, MCI and AD.
Why it matters
In 165 participants with subjective cognitive impairment, mild cognitive impairment, Alzheimer's disease, or mixed dementia, a mobile application using speech tasks and machine learning classified diagnostic groups with accuracy up to 92% for some comparisons. The study concerns assessment and detection, not treating, curing, or preventing Alzheimer's disease.
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Study details
- Participants165
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 46 of the 96 points we could judge it on, which is 48 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Current Alzheimer research) |
| Journal's citation record | 1 / 4 | Current Alzheimer Research: journal h-index 119 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2018 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 165 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 119 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 11.9 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 4.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 43 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Phillippe H. Robert holds only 1 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 1 / 3 | IBM (United States): institution h-index 775 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28847279) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 46 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 46 of the 96 points we could judge it on is 48 out of 100. |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Konig, A., Satt, A., Sorin, A., Hoory, R., Derreumaux, A., David, R., et al. (2018). Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People. Current Alzheimer research. https://doi.org/10.2174/1567205014666170829111942
Vancouver
Konig A, Satt A, Sorin A, Hoory R, Derreumaux A, David R, et al. Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People. Current Alzheimer research. 2018. doi:10.2174/1567205014666170829111942. PMID: 28847279.
BibTeX
@article{konig2018-28847279,
author = {Konig, A. and Satt, A. and Sorin, A. and Hoory, R. and Derreumaux, A. and David, R. and Robert, P. H.},
title = {{Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People}},
journal = {Current Alzheimer research},
year = {2018},
doi = {10.2174/1567205014666170829111942},
pmid = {28847279},
url = {https://doi.org/10.2174/1567205014666170829111942}
}
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