Computerized Analysis of Speech and Language to Identify Psycholinguistic Correlates of Frontotemporal Lobar Degeneration
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OBJECTIVE: To evaluate the use of a semiautomated computerized system for measuring speech and language characteristics in patients with frontotemporal lobar degeneration (FTLD). BACKGROUND: FTLD is a heterogeneous disorder comprising at least 3 variants. Computerized assessment of spontaneous verbal descriptions by patients with FTLD offers a detailed and reproducible view of the underlying cognitive deficits. METHODS: Audiorecorded speech samples of 38 patients from 3 participating medical centers were elicited using the Cookie Theft stimulus. Each patient underwent a battery of neuropsychologic tests. The audio was analyzed by the computerized system to measure 15 speech and language variables. Analysis of variance was used to identify characteristics with significant differences in means between FTLD variants. Factor analysis was used to examine the implicit relations between subsets of the variables. RESULTS: Semiautomated measurements of pause-to-word ratio and pronoun-to-noun ratio were able to discriminate between some of the FTLD variants. Principal component analysis of all 14 variables suggested 4 subjectively defined components (length, hesitancy, empty content, grammaticality) corresponding to the phenomenology of FTLD variants. CONCLUSION: Semiautomated language and speech analysis is a promising novel approach to neuropsychologic assessment that offers a valuable contribution to the toolbox of researchers in dementia and other neurodegenerative disorders.
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Hernández-Domínguez et al. found that generated features were significantly correlated with MMSE severity and achieved an AUC of approximately 0.76 for classifying Alzheimer's disease, mild cognitive impairment, and healthy controls.
“The resulting features were significantly correlated with severity as assessed by the MMSE, and classification performance was characterized by the receiver operating characteristic (ROC) area under curve (AUC) performance of AUC ≈ 0.76 for all three groups.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [33]
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- Authors
- Pakhomov, S. V. S., Smith, G. E., Chacon, D., Feliciano, Y., Graff-Radford, N., Caselli, R., & Knopman, D. S.
- Affiliations
- University of Minnesota (US)
- Mayo Clinic (US)
- University of Puerto Rico, Medical Sciences Campus (PR)
- Corresponding author
- Pakhomov, Serguei V. S.
- Venue
- Cognitive and Behavioral Neurology
- Published
- 2010-09
- Volume
- 23
- Issue
- 3
- Pages
- 165-177
- ISSN
- 1543-3633 (ISSN-L)
- PMC
- PMC3365864
- OpenAlex
- W1986297645
- Cited by
- 64
- References
- 72
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 2 · 2022: 4 · 2021: 6 · 2020: 7 · 2019: 4 · 2018: 5 · 2017: 6 · 2016: 7 · 2015: 4 · 2014: 5 · 2013: 3 · 2012: 4
- FWCI
- 1.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cognitive and Behavioral Neurology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (U01AG016976, P30AG019610, R01AG023195, P50AG016574)
- 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 › Medicine › Physiology › Voice and Speech Disorders
- Keywords
- frontotemporal lobar degeneration, linguistic analysis, neuropsychological assessment, dementia, neurodegenerative diseases, grammaticality
- MeSH terms
- Diagnosis, Computer-Assisted, Humans, Language Tests, Neuropsychological Tests, Psycholinguistics, Software Validation, Speech, Speech Production Measurement, Verbal Behavior, Principal Component Analysis, Speech Recognition Software, Frontotemporal Lobar Degeneration
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.
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