Bibliographic record
Abstract
This study investigated the effects of L1 and L2 experience on the perceptual assimilation of non-native tones by native Mandarin or Thai listeners. Of these, 32 had 0.5–1.5 years of L2 learning experience while 40 had none. All listeners participated in a tonal assimilation task in which they first identified which tone in Mandarin or Thai sounded most similar to the Thai or Mandarin tone they heard, and then rated its goodness on a five point Likert scale. Stimuli included four Mandarin tones (high level, rising, falling-rising, and falling tones) and five Thai tones (mid, low-, falling, high-, and rising tones) on four monosyllables: /tuo/, /fej/, /kha/, /siau/, and a hum. The Thai listeners, regardless of L2 experience, assimilated more L1 tone categories to L2 than did the Mandarin listeners. Nonetheless, the experienced listeners from the two languages showed a high degree of consistency in terms of which tones were assimilated. The perceived similarities between native and non-native tones were not always predictable from their acoustic similarities, and varied with L1 and L2 experience. Results will be discussed in terms of some well-known perceptual assimilation models, such as PAM. [Work supported by GIS.]
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".