A comparison of clinician assessment of speech versus automated speech analysis in mild cognitive impairment and Alzheimer’s dementia
Bibliographic record
Abstract
Abstract Background Language impairment is an important marker of neurocognitive disorders. Despite this, there is no universal system of terminology used to describe speech impairment and large inter‐rater variability can exist between clinicians assessing speech. The role of automated speech analysis is emerging as a novel and potentially more objective method to assess speech in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). No studies have analyzed whether variables extracted through automated speech analysis can also be correlated to speech characteristics identified by a clinician. We sought to: (1) investigate whether clinician‐identified speech characteristics could be correlated with acoustic and linguistic variables identified through automated speech analysis, and (2) through automated speech analysis, identify novel acoustic and linguistic variables that may be associated with MCI or AD. Method Using the DementiaBank speech corpus (Cookie Theft picture description task), audio recordings from patients with possible/probable AD (n=10), MCI (n=10), and controls (n=10) were rated by clinicians. Four characteristics of speech were rated by clinicians, including: word‐finding difficulty, coherence, perseveration, and speech errors. Clinicians were blinded to each other’s scoring. Where scores differed, a consensus rating was established. The speech recordings were then transcribed, and linguistic and acoustic variables were extracted through automated speech analysis. The correlation between clinician‐identified speech characteristics and the acoustic and linguistic variables were then compared. Result A significant correlation (p < 0.05) was found between clinician‐identified speech characteristics and variables extracted through automated analysis. Average word length was correlated with word‐finding difficulty (ρ = 0.74); changes in syntactic construction (use of past tense, third person singular verbs, and subordinate clauses) were correlated with coherence (ρ = 0.51) and errors in speech (ρ = 0.58); similarity between consecutive utterances was correlated to perseveration (ρ = 0.68). Conclusion In this exploratory study, variables extracted through automated acoustic and linguistic analysis of MCI and AD speech were strongly correlated to clinician‐identified speech characteristics. Our results suggest further investigation for using an automated, data‐driven approach to define and monitor subjective clinical speech descriptors in neurocognitive disorders.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".