A regression approach to vowel normalization for missing and unbalanced data
Bibliographic record
Abstract
Researchers investigating the vowel systems of languages or dialects frequently employ normalization methods to minimize between-speaker variability in formant patterns while preserving between-phoneme separation and (socio-)dialectal variation. Here two methods are considered: log-mean and Lobanov normalization. Although both of these methods express formants in a speaker-dependent space, the methods differ in their complexity and in their implied models of human vowel-perception. Typical implementations of these methods rely on balanced data across speakers so that researchers may have to reduce the data available in the analyses in missing-data situations. Here, an alternative method is proposed for the normalization of vowels using the log-mean method in a linear-regression framework. The performance of the traditional approaches to log-mean and Lobanov normalization against the regression approach to the log-mean method using naturalistic, simulated vowel-data was investigated. The results indicate that the Lobanov method likely removes legitimate linguistic variation from vowel data and often provides very noisy estimates of the actual vowel quality associated with individual tokens. The authors further argue that the Lobanov method is too complex to represent a plausible model of human vowel perception, and so is unlikely to provide results that reflect the true perceptual organization of linguistic data.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".