Speaking upside down: Manipulation of vowel formants with an inversion table
Bibliographic record
Abstract
Posture affects the direction of gravitational forces on articulators. Since speakers seldom talk upside down, inverted posture offers an opportunity to observe the response of the neural control system to unfamiliar posture perturbations. Twenty native English speakers (16 women; 4 men) aged between 20 and 30 years gave written informed consent to participate in the study. They reported no history of hearing, balance, or speech problems. The participants were secured on an inversion table which was rotated and fixed to three positions: upright, supine, and upside down. The order of the postures was randomized. A headphone mounted microphone recorded series of ten repetitions of three words with target vowels /i,u,a/ embedded in a carrier word /hVd/ in each posture. Formants were extracted from the middle of the vowel using Praat. Results and discussion: Every participant's productions were impacted systematically by posture with greater acoustical effects observed for the unfamiliar posture. Based on mixed effects models, the F1 and F2 responses between participants aligned for /i/ in upside down condition compared to upright posture. The data suggests that unfamiliar (upside down) postural perturbation leads to greater deviation from its acoustic target than a familiar (supine) postural perturbation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".