An Analysis of Acoustic Features for Attention Score in Thai MoCA Assessment
Bibliographic record
Abstract
Screening tests like the Montreal Cognitive Assessment (MoCA) can help diagnose mild cognitive impairment (MCI). MoCA comprises subtests that span various cognitive domains. Numerous researchers attempt to detect MCI by employing speech-related features such as acoustic, linguistic, and prosodic features. However, the features can distinguish patients with MCI from healthy people but do not describe each patient's specific cognitive domain impairment. This study focuses on Digit Backward Span (DBS) and Digit Forward Span (DFS), subtests related to the cognitive attention domain in MoCA. We develop a model and identify the most relevant speech features for the domain from a recorded voice from these subtests in the Thai MoCA. We rank features by their importance and found that using a subset of important features has higher predictive power than using the entire feature set in impairment in the attention domain. The most important features in both tests are the median duration of voice and the duration of voice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".