Perception and imitation of prevoicing across language backgrounds
Bibliographic record
Abstract
The present study investigates speakers’ ability to perceive and imitate prevoicing, testing speakers of languages differing its contrastive status: those where prevoicing serves as a primary cue to the laryngeal contrast (Indo-Aryan languages; “true voicing” languages, e.g., Tagalog), those where prevoicing never occurs (“aspirating” languages, e.g., Cantonese), and those where prevoicing occurs in free variation (other aspirating languages, e.g., English). After hearing pairs of Hindi words differing in presence/absence of prevoicing, participants were asked to imitate them, followed by an ABX discrimination task. Preliminary findings show above-chance discrimination accuracy as well as significant imitation of the difference in participants all language backgrounds, with place of articulation, consonantal, and vocalic cues all affecting the extent of prevoicing in production. As expected, speakers of languages where prevoicing is absent showed less overall prevoicing than other groups; however, there were not clear differences across language groups in discrimination ability. While faithful imitation was usually associated with accurate discrimination, good discrimination was also found on many tokens that were not faithfully imitated, indicating that other (production-based) factors are necessary to account for variability in imitative ability.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".