Applying refined automatic formant measurement to determination of the orientations of vowel distributions
Bibliographic record
Abstract
In order to remedy recurrent problems with false formant readings obtained with an automatic formant measurement routine, prototype-based automatic measurements were compared with manual formant measurements of the same uttered vowels. Two refinements that avoided false formants, one involving the option to skip measured formant racks and the other involving an expectation that successive formants would show successively lower amplitudes, were developed. This method was then applied to seven corpora representing diverse English dialects, with satisfactory results. The measurements of each vowel thus obtained were then subjected to principle component analysis to determine the orientation of the tokens in F1/F2 space. Most vowels exhibited distributions that apparently reflect degree of jaw opening. However, /u/ in North American varieties (with pre-/l/ and post-/j/ tokens excluded) showed mostly horizontal orientations, even when post-coronal and non-post-coronal tokens were considered separately. This pattern contrasted sharply with the vertical orientations of mid back vowels. /u/ was also the only vowel whose orientations coincided consistently with ongoing changes in the communities. We hypothesize that the factors responsible for the horizontal orientations of /u/ also lie behind its cross-linguistic tendency to shift frontward.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".