Acoustic analysis of the speech of patients with oral cancer: A methodological investigation comparing speech stimuli derived from different clinical outcome measures
Bibliographic record
Abstract
Background: Oral cavity cancer and its treatment reduce the quality of speech. Clinicians often utilize a variety of outcomes measures to assess speech function of patients with oral cavity cancer reducing the comparability of formant results due to differences in linguistics and phonetic contexts. Objective: To evaluate the degree of agreement in vowel space size in a F1-F2 plane, when comparing speech stimuli selected from a controlled environment (i.e., /hVd/ context) to speech stimuli segmented from two clinically available speech outcome measures in patients treated for cancer of the oral tongue. Methods: Voice recordings of nine patients treated with primary surgery for cancer of the oral cavity who attended functional assessment appointments (pre-operatively, and at 1-, 6-, and 12-months post-operatively) were analyzed. Agreement between vowel space size obtained from /hVd/ phrases and other speech assessments were compared using linear correlations and t-tests. Results: Vowel space size derived from /hVd/ phrases correlates strongly with corresponding estimates from other speech tasks (r = 0.72 to 0.82, p <0.0001). The other tasks had significantly reduced vowel space sizes compared to /hVd/ (t = 4.7 to 5, p<0.0001). Conclusion: Vowel space size estimates based on different speech tasks are mutually comparable and may offer insight to oral cancer speech production acoustics.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".