Effects of linguistic and musical experience on non-native perception of Thai vowel duration.
Bibliographic record
Abstract
Previous research has suggested a relationship between musical experience and L2 proficiency. The present study investigated the influence of musical experience on non-native perception of speaking-rate varied Thai phonemic vowel length distinctions. Given that musicians are trained to discern temporal distinctions in music, we hypothesized that their musical experience would enhance their ability to perceive non-native vowel length distinctions as well as their sensitivity to temporal changes as a function of speaking rate. Naive native English listeners of Thai, with and without musical training, as well as native Thai listeners, were presented with minimal pairs of monosyllabic Thai words differing in vowel length at three speaking rates in an identification task and a discrimination task. For identification, participants were asked to identify whether a word contained a long or short vowel. For discrimination, participants heard three successive words and were asked to indicate whether the second word had the same vowel length as the first or last word. The results show significant group differences in identification and discrimination accuracy within and across speaking rates, suggesting that listeners’ perception of phonetic categorical versus temporal acoustic variations differs as a function of linguistic and musical experience.
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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 | 0.004 |
| 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 | 0.001 |
| 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".