The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.
A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech
Open accessAbstract
Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“They were able to achieve over 80% classification accuracy by selecting LIWC categories that best represented the difference in the MCI and healthy control datasets.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [7]
In the citing paper
“It is clear from the literature that first-order lexeme-level features, i.e. those related to lexical diversity and density, are useful biomarkers for detecting the presence or predicting the onset of a variety of conditions, such as MCI, AD, CTE, and potentially several others.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,012
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,852
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,007
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,916
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417
A new rating scale for Alzheimer's disease
(1984) · American Journal of Psychiatry · doi:10.1176/ajp.141.11.1356 · Cited by 4,175
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140
Research works printed but not linked: 3
- [36]Hall M. Frank E. Holmes G. Pfahringer B. Reutemann P. Witten I. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 2009 11 1 10 18
- [37]Freedman M.I. Leach L. Kaplan E. Winocur G. Shulman K.J. Delis D.C. Clock drawing. Oxford Oxford University Press 1994
- [44]Szabó L. Tarján B. Mihajlik P. Fegyó T. Speeding up error correction in a Hungarian dictation system (in Hungarian Proceedings of MSZNY 182 91 2015
Cited by
Sorted by citations
García, S. D. L. F., Ritchie, C. W., & Luz, S. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06047 · Cited by 265
Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice
Fagherazzi, G., Fischer, A., Ismael, M., & Despotović, V. (2021) · Digital Biomarkers · doi:10.1159/000515346 · Cited by 249
Graham, S. A., Lee, E., Jeste, D. V., Patten, R. V., Twamley, E. W., Nebeker, C., Yamada, Y., Kim, H., & Depp, C. A. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.112732 · Cited by 238
Haider, F., García, S. D. L. F., & Luz, S. F. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2955022 · Cited by 204
Predicting mild cognitive impairment from spontaneous spoken utterances
Asgari, M., Kaye, J., & Dodge, H. (2017) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1016/j.trci.2017.01.006 · Cited by 185
Metadata
- Authors
- Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z., Pakaski, M., & Kalman, J.
- Affiliations
- MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
- Linguistics Department, University of Szeged, Szeged, Hungary
- MTA-SZTE Research Group on Artificial Intelligence (HU)
- Department of Psychiatry, University of Szeged, Szeged, Hungary
- Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
- ORCID
- Vincze, Veronika
- Venue
- Current Alzheimer Research
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2018-01-03
- Volume
- 15
- Issue
- 2
- Pages
- 130-138
- ISSN
- 1567-2050 (ISSN-L)
- PMC
- PMC5815089
- OpenAlex
- W2770604845
- Cited by
- 290
- References
- 49
- Citations by year
- 2026: 39 · 2025: 37 · 2024: 42 · 2023: 46 · 2022: 31 · 2021: 44 · 2020: 26 · 2019: 19 · 2018: 3
- FWCI
- 11.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Current Alzheimer Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- Funders
- Magyar Tudományos Akadémia
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- mild cognitive impairment, Alzheimer's disease, spontaneous speech, speech tempo, acoustic features, speech hesitations, pauses, neuropsychological screening, automated detection, memory retrieval
- MeSH terms
- Machine Learning, Aged, Aged, 80 and over, Diagnosis, Computer-Assisted, Female, Humans, Male, Memory, Middle Aged, Neuropsychological Tests, Pattern Recognition, Automated, ROC Curve, Speech, Speech Production Measurement, Models, Statistical, Internet, Speech Recognition Software, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
