A CROSS-LANGUAGE VOWEL NORMALISATION PROCEDURE
Bibliographic record
Abstract
A variation of log-mean normalization that can be applied in cross-language and cross-dialect experiments, is presented. Vowel normalization procedures seek to remove inter-speaker variance due to factors such as vocal tract size, which human listeners discount while identifying vowels. It is expected that the mean vocal-tract length are approximately equal over large and sufficiently balanced samples of speakers. The cross-language normalization procedure is tested using acoustic data from productions of L1-Spanish non-low front vowels and L1 English non-low front vowels. Three models are trained and tested using non-normalized log-Hertz values, language-normalized values, and cross-language normalized values respectively. The models trained on normalized L1-English vowels had a slightly higher correct-classification rate on the training data than the model trained on non-normalized data. The vowel normalization increased the correlation between monolingual English listeners.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.019 |
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".