Thai Rate-Varied Vowel Length Perception and the Impact of Musical Experience
Bibliographic record
Abstract
Musical experience has been demonstrated to play a significant role in the perception of non-native speech contrasts. The present study examined whether or not musical experience facilitated the normalization of speaking rate in the perception of non-native phonemic vowel length contrasts. Native English musicians and non-musicians (as well as native Thai control listeners) completed identification and AX (same-different) discrimination tasks with Thai vowels contrasting in phonemic length at three speaking rates. Results revealed facilitative effects of musical experience in the perception of Thai vowel length categories. Specifically, the English musicians patterned similarly to the native Thai listeners, demonstrating higher accuracy at identifying and discriminating between-category vowel length distinctions than at discriminating within-category durational differences due to speaking rate variations. The English musicians also outperformed non-musicians at between-category vowel length discriminations across speaking rates, indicating musicians' superiority in perceiving categorical phonemic length differences. These results suggest that musicians' attunement to rhythmic and temporal information in music transferred to facilitating their ability to normalize contextual quantitative variations (due to speaking rate) and perceive non-native temporal phonemic contrasts.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".