Articulatory and acoustic variability in Latin American Spanish vowel production
Bibliographic record
Abstract
This study investigates the relationship between articulatory and acoustic variability in Latin American Spanish vowel production. While Whalen et al. (2018) found that American English vowels show similar degrees of variability across domains and a correlation between them, it remains unclear whether this pattern generalizes across languages. We analyzed audio, tongue ultrasound, and lip video data from 11 native speakers reading a word list. Tongue and lip contours were semi-automatically tracked using DeepLabCut [Mathis, et al. 2018] in AAA [Articulate Instruments Ltd. 2012], and vowel formants were extracted at midpoint. For each word, mean formant values and tongue and lip contours were computed, and variability across repetitions was quantified using the coefficient of variation (CV). Preliminary results from eight speakers showed no statistically significant correlation between tongue and formant variability (Pearson’s r = −0.64, p = 0.087). A linear mixed-effects model revealed marginally higher CV in articulation than in acoustics (p = 0.071), with no significant vowel-specific effects. The weaker correlation compared to English suggests that the correspondence between articulatory and acoustic variability may differ cross-linguistically, highlighting the need to expand research to languages with diverse sound systems. This work contributes to a broader understanding of cross-linguistic variability patterns in speech production.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".