Weak adaptation to foreign-accented voice-onset-time distribution
Bibliographic record
Abstract
Listeners quickly adapt to foreign-accented speech in transcriptions and word/sentence judgments (Bradlow & Bent, 2008; Clarke & Garrett, 2004; Sidaras, Alexander, & Nygaard, 2009). However, continuous spoken-word recognition measures such as eye tracking have revealed limitations on foreign-accent adaptation. For example, Trude, Tremblay, and Brown-Schmidt (2013) found that English listeners’ adaptation to a second-order constraint in a Quebec French talker’s accent (/i/ = [ɪ] before coda consonants except voiced fricatives) was limited. This study investigates whether foreign-accent adaptation is less limited when the mapping between the accented and underlying words does not require higher-level inferencing. English listeners completed an eye-tracking experiment in which they heard a (female) French talker and a (male) English talker. Target and competitor words began with a voiced or voiceless stop and were otherwise temporarily ambiguous (e.g., timberand dimple). All stops were resynthesized: The French talker’s stops were prevoiced (voiced) and short-lag (voiceless) and the English talker’s stops were short-lag (voiced) and long-lag (voiceless). Preliminary results suggest an effect of voicing only for the French talker, with voiceless-stop targets generating more competition than voiced-stop targets. Importantly, this effect decreased only slightly from the first to the second half of the experiment, suggesting weak foreign-accent adaptation.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".