Spanish listeners’ use of vowel spectral properties as cues to post-vocalic consonant voicing in English
Bibliographic record
Abstract
Mexican Spanish listeners who had just arrived in an Anglophone region of Canada were tested on an edited-natural-speech continuum for Canadian English /bit bIt bid bId/ in which vowel duration and vowel spectral properties were varied, and in which consonant closures were silent. The Mexican Spanish listeners did not use vowel spectral properties to identify the vowel: Vowel identification was near chance level with a tendency for longer vowel stimuli to be identified as /i/. However, they did use vowel spectral properties to identify the consonant: Stimuli containing vowels with low F1 were identified as having voiceless consonants, and stimuli containing vowels with high F1 were identified as having voiced consonants. This presentation will consider possible explanations for this identification pattern for consonant voicing, and will also present the results of additional tests conducted with the same participants, namely: an identical perception test conducted 6 months after the participants’ arrival in Canada, English production tests using the same words conducted at the same time as the two perception tests, and a Spanish production test using the words /bit bid bíti bídi bití bidí/ conducted at the same time as the first set of English tests.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".