Speech changes in neurodegenerative diseases relate to clinical outcomes
Bibliographic record
Abstract
BACKGROUND: The detection and characterization of speech changes may help in the identification of neurodegenerative diseases and have the potential to help with patient characterization and monitoring. Yet, there is limited research validating the presence of speech changes across different types of neurodegenerative disease. We report on the relationships between speech and other clinical assessments in the individuals with different dementia diagnoses and in comparison to healthy older adults. METHOD: We analyzed speech recordings from 109 patients (52F, 57M; Age = 72.63± 8.61) who were diagnosed with various neurodegenerative diseases, including Alzheimer's disease, Frontotemporal Dementia, and Vascular Cognitive Impairment, in a cognitive neurology memory clinic. Speech recordings of an open-ended picture description task were processed using the Winterlight speech analysis platform which generates >500 acoustic and linguistic features. We investigated the linear relationship between the speech features and clinical assessments including the Mini Mental State Examination (MMSE), Western Aphasia Battery (WAB), and Mattis Dementia Rating Scale while controlling for age, sex and years of education. Speech features that were significantly associated with clinical measures were then included in group comparisons with healthy older adults (N = 74, ∼39F; Age = 61.31±7.29). RESULT: Speech features including lexical and syntactic features were significantly correlated with clinical assessments in patients, across diagnoses. Lower MMSE scores were associated with the use of more familiar nouns (β = -1.60, p<.001). Similarly, increased impairment as assessed by the WAB was correlated with the use of higher frequency nouns (β = -0.01, p<.001). Patients used significantly more nouns (z = 6.25, p<.001) and shorter words (z = 8.33, p<.001) than the healthy older adults. Their speech duration was also significantly shorter (z = 7.98, p<.001) and they paused more (z = 5.19, p<.001). CONCLUSION: Speech changes representing decreased speech, with simpler vocabularies and syntax, were detectable in patients with different neurodegenerative diseases and correlated with clinical outcomes. These same speech patterns differed in patients with neurodegenerative disease compared to healthy older adults. Speech has the potential to be a sensitive measure for detecting cognitive impairments across various neurodegenerative diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".