Prosodic characteristics of English speakers with Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. A large amount of work has focused on the automatic detection of AD, but few have examined the underlying acoustic correlates. Through the analysis of 42 speakers (21 AD and 21 control), research by Martinez-Sanchez et al. have reported flattened pitch trajectories which distinguish Spanish-speaking AD patients from those of controls [Martinez-Sanchez et al., Psicothema 24(1), 16–21 (2012)]. The present study aims to determine whether a similar effect is found in a large-scale study of English speakers with AD. Prosogram [Mertens, in Proceedings of Speech Prosody (2004)] was used to assess pitch trajectories of 203 English-speaking participants (128 AD and 75 control) from Dementiabank’s Pitt Corpus [Becker et al., Arch. Neurol. 51(6), 585–594 (1994)]. AD patients exhibited greater pitch range and trajectories across phonation than controls, both within and across syllables. These preliminary findings conflict with previous observations of a flattened prosodic profile for AD patients. A possible reason for this discrepancy is a difference in speech elicitation tasks. Previous studies on Spanish-speakers used reading and delayed pronunciation tasks, whereas the present study utilizes a picture description task, a more natural elicitation method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".