A regression approach to vowel normalization for missing and unbalanced data
Bibliographic record
Abstract
Researchers investigating the vocalic systems of languages or dialects frequently employ normalization methods to minimize between-speaker variability in formant patterns while largely preserving dialectal, between-phoneme variability. One popular method, log-mean normalization, relies on estimating the logarithm of the geometric mean formant-frequency (Gs) produced by a speaker across their vocalic inventory, and then expressing the formant patterns produced by a speaker as deviations from this mean. However, in the face of missing or unbalanced data, the traditional approach to calculating Gs for a speaker will usually lead to biased estimates, which will produce artificial asymmetries in the normalized vowel spaces of different speakers. An alternative method is proposed for the estimation of Gs based on a linear-regression framework, which avoids the biases associated with traditional estimation of Gs when data is unbalanced. The regression method to normalization is described, and simulations are carried out to compare the accuracy of Gs estimates via regression to other estimation methods. Results indicate that the proposed method is substantially more accurate than the traditional approach to estimating Gs in the face of missing or unbalanced data.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".