Measuring articulatory similarity with algorithmically reweighted principal component analysis.
Bibliographic record
Abstract
Articulatory similarity was assessed for a corpus of 2700 ultrasound images of 75 cross-linguistically frequent speech sounds produced by four subjects in three segmental contexts (a_a, i_i, u_u). Cross-distances were generated for the entire length of the vocal tract using the Palatron algorithm and realistic estimates of the location of the pharyngeal wall and teeth, resulting, after interpolation, in 60 cross-distances per token. A standard principal component analysis of these data is overwhelmed by the coarticulatory effects of the context vowels. Algorithmically reweighted principal component analysis was devised in order to use coarticulatory variation to isolate the most distinctive cross-distances for each target segment. The reweighting algorithm considers the variance across repetitions in each of the 60 cross-distances, as well as variance in the slope of the cross-distance function, in order to identify areas of stability across tokens. For each target sound, cross-distances with the greatest variance are reset to the mean for all target segments, while cross-distances with the least variance maintain their original values. This has the effect of defining each target sound by the cross-distances, which are most stable across contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".