Articulatory compensation to second formant perturbations
Bibliographic record
Abstract
There is a fast-growing literature examining speakers? response to real-time alterations of auditory feedback. The majority of these studies examine the response of the subject in acoustic terms. Since many subjects fail to (acoustically) compensate for the perturbation, the current experiment examines whether there are systematic articulatory responses to formant perturbation in the absence of compensation at the level of acoustics. Articulatory data is collected using a 3D Electro-magnetic-articulograph. F2 is gradually shifted up or down and preliminary results from 3 English-speaking subjects showed that two subjects show no response in their acoustics or articulation. However, the remaining speaker who did not show compensation at the level of acoustics displayed a systematic response in some articulatory variables. The acoustic effects of his response were masked because the other articulators behaved in a more variable way, making the second formant vary randomly from trial to trial. Based on these results we expect to see a spectrum of response patterns from a larger population of speakers, from total non-compensation in both acoustics and articulation, partial compensation in articulation, and global articulatory compensation, which induces the appropriate compensation at the level of acoustic output.
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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) | 0.001 | 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) | 0.002 | 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".