Acoustic characterization of the Costa Rican Non-Standard Trills
Bibliographic record
Abstract
This study characterizes the Costa Rican Spanish trill realizations acoustically, identifies the number of variants, and determines if they are acoustically distinguishable. Costa Rican trills typically lack vibration of the tongue tip and are thus categorized as ‘non-canonical’ compared to the normative rolled trill. Only impressionistic descriptions have been provided, posing challenges to comparisons across studies and dialects. Using Audacity, 18 speakers (9 female, M = 37.4) recorded their productions of 72 tokens containing the trill in word-initial/medial position and in stressed/unstressed syllables. The study was conducted remotely through Gorilla Experiment Builder. Duration, percentage of voicing and center of gravity were measured in PRAAT. Classification was based on visual inspection of the spectrograms and waveform, with the audio as a guide. ANOVAs were used to examine the distribution of the variants and their acoustic differences. Ten categories are characterized acoustically, the fricative being the most common variant. Stress made realizations longer but did not reach significance. Word-initially, fricatives and trills had a significantly higher percentage of devoicing, and while approximants and trills tended to be shorter in this position, fricatives had the opposite direction of effect. Fricatives also showed considerable variation in center of gravity (M = 2159.2 Hz, Range = 5622.3 Hz). This acoustic description of the non-standard variants serves as a parameter for future comparisons with other dialects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".