Bibliographic record
Abstract
Multiple measures of vowel overlap have been proposed that use F1, F2, and duration to calculate the degree of overlap between vowel categories. The present study assesses four of these measures: the spectral overlap assessment metric [SOAM; Wassink (2006). J. Acoust. Soc. Am. 119(4), 2334-2350], the a posteriori probability (APP)-based metric [Morrison (2008). J. Acoust. Soc. Am. 123(1), 37-40], the vowel overlap analysis with convex hulls method [VOACH; Haynes and Taylor, (2014). J. Acoust. Soc. Am. 136(2), 883-891], and the Pillai score as first used for vowel overlap by Hay, Warren, and Drager [(2006). J. Phonetics 34(4), 458-484]. Summaries of the measures are presented, and theoretical critiques of them are performed, concluding that the APP-based metric and Pillai score are theoretically preferable to SOAM and VOACH. The measures are empirically assessed using accuracy and precision criteria with Monte Carlo simulations. The Pillai score demonstrates the best overall performance in these tests. The potential applications of vowel overlap measures to research scenarios are discussed, including comparisons of vowel productions between different social groups, as well as acoustic investigations into vowel formant trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".