Compensatory articulatory behaviors after tongue reconstruction in production of English plosives: Establishing the range of lip movement patterns for control speakers
Bibliographic record
Abstract
Head and neck cancer can have a devastating impact on speech and swallowing function. In particular, a tumor in the tongue can reduce the ability to produce articulatory gestures typical for English plosives. Previous case studies suggest that the lower lip can compensate for tongue tip gestures in speech after tongue reconstruction. The goal of this study is to establish typical lower lip movement patterns for alveolar and velar plosives for English speakers towards identifying compensatory lip movements in cancer speakers. Ten participants were recruited with no reported hearing or speech problems. A list of 40 minimal pairs beginning with the target plosives were created and embedded in carrier sentences. The participants read sentences in random order with and without babble noise in a standing posture. Lip motion was captured using a custom app on an iPhone 11 capturing the perioral surface area with the TrueDepth infrared camera. Preliminary results suggest that this app-based approach to lip tracking is a viable tool to capture typical lip movement patterns, ultimately enabling clinical research in more accessible settings. Mixed-effects linear regression analyses of the lip kinematics will be discussed.
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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) | 0.000 | 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) | 0.002 | 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".