Digital Voice as an Alternative Screening Tool to the Montreal Cognitive Assessment
Bibliographic record
Abstract
Abstract Background The Montreal Cognitive Assessment (MoCA) is a commonly used screening tool for cognitive impairment. Despite translation into multiple languages to facilitate broader use globally, there are inherent education and cultural biases that result in variations in cognitive screening accuracies. Acoustic voice features are emerging as a more education, language and culturally agnostic indicator of cognitive status, but as a surrogate to the MoCA has not been adequately explored. This pilot study aimed to examine the association between spectral acoustic features extracted by two functionals and MoCA total scores. Method We included 80 participants from the Boston University Alzheimer's Disease Research Center, whose responses to a picture‐description task were digitally recorded and administered the MoCA. Using openSMILE, each recording was divided into 20‐ms frames, using a sliding window that advanced by 10 ms for every segment. For each segment, we extracted 26 RASTA‐style filtered auditory spectrum (bands 1‐26) low‐level descriptors (LLD). Two functionals were applied to the 26 LLD to summarize information across each recording: the mean (mean value of LLD for all segments in each recording) and “upleveltime75” (percentage of time the signal exceeds 75% of the feature range above the minimum). Linear regression models were used to assess the association of these acoustic features with MoCA total scores, adjusting for age, sex, and education. Result Participant demographics included age: mean 68.7, SD 9.27 years; 81.25% college graduate or higher; 60.0% women. Among these participants, 29 were cognitively impaired. The mean MoCA score was 26.49 (SD = 2.54). Among the 26 upleveltime75 functional‐based spectral features, 5 showed significant negative associations with MoCA. The strongest effect was observed for band 25 (beta = –0.77, SE = 0.26, p = 0.0038). In contrast, no significant associations were observed for the acoustic features generated by the mean functional. Conclusion These results suggest that digital voice contains cognitive‐related signals and show promise as a potential globally appropriate cognitive screening tool given the ease in which it can be collected and the availability of automated open‐source tools for analysis. Further exploration analyzing voice recordings across different languages and cultural/education strata are warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".