Cognitive impairment prediction in the elderly based on vocal biomarkers
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Yu et al. achieved accuracy 0.93 on a private dataset using phoneme characteristics, pronunciation coordination characteristics, and pitch variance.
“| Yu et al. [74] | Based on phoneme characteristics, pronunciation coordination characteristics, and pitch variance | speech | private dataset | Accuracy: 0.93 |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [74]
Cites
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Singer, J. D., & Willett, J. B. (2003) · doi:10.1093/acprof:oso/9780195152968.001.0001 · Cited by 10,647 · see citing works
A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., & Larrañaga, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm344 · Cited by 5,252 · see citing works
Discrete-Time Speech Signal Processing: Principles and Practice
Quatieri, T. F. (2001) · CERN Document Server (European Organization for Nuclear Research) · Cited by 836 · see citing works
Self-citationA study of language functioning in Alzheimer patients
Appell, J., KERTESZ, A. E., & Fisman, M. (1982) · Brain and Language · doi:10.1016/0093-934x(82)90006-2 · Cited by 524 · see citing works
A wrapper method for feature selection using Support Vector Machines
Maldonado, S., & Weber, R. W. (2009) · Information Sciences · doi:10.1016/j.ins.2009.02.014 · Cited by 478 · see citing works
Spoken Language Derived Measures for Detecting Mild Cognitive Impairment
Roark, B., Mitchell, M., Hosom, J.-P., Hollingshead, K., & Kaye, J. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2112351 · Cited by 355 · see citing works
Seizure prediction using EEG spatiotemporal correlation structure
Williamson, J. R., Bliss, D. W., Browne, D. W., & Narayanan, J. (2012) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2012.07.007 · Cited by 203 · see citing works
Self-citationVocal biomarkers of depression based on motor incoordination
Williamson, J. R., Quatieri, T. F., Helfer, B. S., Horwitz-Martin, R. L., Yu, B., & Mehta, D. D. (2013) · doi:10.1145/2512530.2512531 · Cited by 189 · see citing works
Self-citationRantala, L. M., Vilkman, E. A., & Bloigu, R. (2002) · Journal of Voice · doi:10.1016/s0892-1997(02)00106-6 · Cited by 153 · see citing works
Cognition, language, and clinical pathological features of non-Alzheimer's dementias: An overview
Reilly, J. J., Rodriguez, A. D., Lamy, M., & Neils‐Strunjas, J. (2010) · Journal of Communication Disorders · doi:10.1016/j.jcomdis.2010.04.011 · Cited by 120 · see citing works
Speech-based automatic and robust detection of very early dementia
Satt, A., Hoory, R., König, A., Aalten, P., & Robert, P. H. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-544 · Cited by 76 · see citing works
Automatic Prosodic Variations Modeling for Language and Dialect Discrimination
Rouas, J.-L. (2007) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2007.900094 · Cited by 56 · see citing works
Prediction of cognitive performance in an animal fluency task based on rate and articulatory markers
Yu, B., Quatieri, T. F., Williamson, J. R., et al. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-270 · Cited by 18 · see citing works
Self-citationDeveloping Dementia Prevention Trials
Sano, M., Egelko, S., Donohue, M., et al. (2012) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3182769c05 · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
An automatic Alzheimer’s disease classifier based on spontaneous spoken English
Bertini, F., Allevi, D., Lutero, G., Calzà, L., & Montesi, D. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2021.101298 · Cited by 66 · see citing works
- Authors
- Yu, B., Quatieri, T. F., Williamson, J. R., & Mundt, J. C.
- Venue
- Interspeech 2015
- Publisher
- ISCA
- Published
- 2015-09-06
- Pages
- 3734-3738
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2407894931
- Cited by
- 28
- References
- 14
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 1 · 2022: 2 · 2021: 4 · 2020: 3 · 2019: 1 · 2018: 3 · 2017: 4 · 2016: 3
- FWCI
- 20.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- cognitive impairment prediction, older adults, vocal biomarkers, speech analysis, dementia risk, voice characteristics
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
