Modelling the intensity difference of Spanish alveolar taps with finite mixture models
Bibliographic record
Abstract
The production of stops has been documented to vary considerably in several languages. The difference between the minimum intensity during the consonant and the maximum intensity of the surrounding vowels is an acoustic correlate of taps, whose realizations include plosives, approximants, and deletions. We investigated how lexical, phonetic, and predictability-related factors are associated with changes in the intensity difference of Spanish taps. We conducted an acoustic analysis using a force-aligned corpus of conversational Spanish, with ten percent of tokens hand-corrected to evaluate performance. Model checking with generalized linear models indicated that one distribution could not adequately account for the observed data. As such, we analyzed variation in intensity difference using finite mixture models comprising two skew-normal distributions, which provided a substantially better fit. The results of our modelling approach require a more nuanced interpretation than standard linear models. Our model indicates that Spanish tap production is a complex system where some variables, like frequency, are related to categorical shifts between two potential realizations, and other variables are related to gradient intensity changes within each potential realization of the tap. We also find that articulatory factors like speech rate are responsible for larger intensity changes than lexical properties like frequency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".