The Acoustic Realization of the Stop Voicing Contrast in Argentine Spanish
Bibliographic record
Abstract
Consonant lenition is a synchronic and diachronic sound change in which consonants become "weaker" or more vowel-like in certain contexts, especially between vowels.Given the variability in how Spanish dialects lenite the voiced stops and given that some varieties of Spanish have been shown to weaken the voiceless stops too, this raises the question of how different dialects of Spanish realize the stop voicing contrast.This thesis explores this issue for Argentine Spanish.This study is based on the corpus data with 9 speakers, 400 recordings containing the 6 stops in intervocalic position.The Acoustic analysis is done in Praat, and relative intensity and percent voicing were measured.The findings indicate that the stop voicing contrast is realized through a combination of relative intensity and percent voice with relative intensity being the stronger of the two cues.Also, place of articulation does not affect the stop voicing contrast in this variety.This study contributes to our understanding of lenition processes and contrast maintenance in varieties of Spanish by illustrating how the stop voicing contrast is realized in one particular variety. 1.3Research questions ...
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".