Perceived nativeness and sensitivity to temporal adjustments in speech
Bibliographic record
Abstract
Native Mandarin Chinese speakers productions of English consonant-vowel (CV) syllables have shown syllable-internal temporal adjustments in the direction of native (English)-like CVs (Wang and Behne, 2004). The current study presents two experiments investigating whether these temporal adjustments affect perceived nativeness. For three production types (native-English, Chinese productions of English, native-Chinese), three syllable-internal timing patterns (English-like, Chinese-English-like, Chinese-like) were applied, resulting in nine stimuli types. Native English listeners judged how English-like each stimulus was on a 7-point scale. In the first experiment, production-types and timing patterns were randomized. Results show that listeners can reliably identify nativeness of the three productions, with Chinese productions of English perceived as intermediate to the native Chinese and native American English productions. Listeners also showed a tendency toward using timing within the CV to identify nativeness. In the second experiment the same materials were therefore blocked by production type. Results reveal the perceptual saliency of the temporal adjustments in nonnative productions. These findings support a view of L2 acquisition as a gradual process toward the target L2 (e.g., Caramazza et al., 1973). The current study extends this view, showing evidence that listeners can perceive the inter-language system, bearing the nature of both L1 and L2.
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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 | 0.006 |
| 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) | 0.001 | 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".