Assessing head-and-neck cancer patient speech with the vowel dispersion index
Bibliographic record
Abstract
The present study uses a measure of the dispersion of density throughout the vowel space—called the vowel dispersion index—to assess speech patterns in head-and-neck cancer patients. The vowel dispersion index is based on calculating the total variation of the density values in Story and Bunton’s (2017) convex hull representation of vowel space density. Overall, the vowel dispersion index quantifies how much change there is throughout the vowel space density. The vowel dispersion index is calculated and analyzed for a sample of 333 recordings of the zoo passage from 107 head-and-neck cancer patients at different stages pre- and post-surgery. Linear mixed-effects regression suggests that the vowel dispersion index is not greatly influenced by the time elapsed since a patient underwent surgery. In contrast, vowel space area is reduced following surgery. These trends suggest that patients retain control of the dispersion of their vowels throughout the vowel space, even after surgery. Their vowel space area does place a constraint on the degree to which they can disperse their vowel tokens, however. These findings are discussed with respect to phonetic theory, principally, Lindblom’s (1990) H&H theory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 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".