Random effects and the evaluation of the uniform scaling hypothesis for vowels
Bibliographic record
Abstract
The uniform scaling hypothesis suggests that formant frequencies of vowels of one speaker can accurately predict those of any other speaker of the same dialect by applying a single multiplicative scale factor. Fant (STL QPSR, 2-3, 1-19, 1975) questioned this assumption and presented graphical evidence that scale factors vary between adult men and women for different vowels in ways that are systematically related to their position in the vowel space. However, the issue is complicated by the fact that average vowel datasets may contain multiple sources of variation, including dialect mixture. Statistical evaluation of the uniform scaling hypothesis (or any systematic deviations from it) need to account for multiple sources of variation. In preliminary random-effects analyses of formant data produced by individual speakers from three geographically distinct dialect regions of American English, we found that while relatively modest systematic trends in nonuniformity may exist, their magnitude may be smaller than suggested by earlier work and their assessment may be strongly influenced by other sources of variation within geographical dialect regions. We will present extensions of our preliminary analyses that will include speech data from Texas, Alberta, and Nova Scotia collected in our laboratories.
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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 | 0.096 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".