Predicting scores on the Montreal cognitive assessment from a spontaneous speech sample
Bibliographic record
Abstract
This presentation examines acoustic and linguistic measurements taken from 521 older participants. The aim was to examine the relationship between measurements derived from spontaneous speech and participants’ scores on the Montreal Cognitive Assessment (MOCA). Though both speech measurements and MOCA scores have been shown to be predictive of mild cognitive impairment, there has been little exploration in the literature of the relationship between these two domains. Participants completed the MOCA assessment and were prompted to describe an early childhood memory. The lexical complexity analyzer toolkit was used to extract a range of measurements to assess the lexical density, sophistication, and variation in each participant’s storytelling passage. For the acoustic analysis, we drew a subset of the features from the Interspeech 2010 Paralinguistic Challenge set and included measures of duration and speaking rate. A least absolute shrinkage and selection operator (LASSO) approach was used to model the relationship between acoustic, lexical, and demographic information and participants’ MOCA scores. Using the covariance test statistic we identified six important variables: one lexical, three acoustic and two demographic. This reduced model explained 18.06% of the variance in participants’ MOCA scores. This R2 value was validated on a held out set of 121 participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".