Spoken word recognition in a second language: The importance of phonetic details
Bibliographic record
Abstract
Spoken word recognition depends on variations in fine-grained phonetics as listeners decode speech. However, many models of second language (L2) speech perception focus on units such as isolated syllables, and not on words. In two eye-tracking experiments, we investigated how fine-grained phonetic details (i.e. duration of nasalization on contrastive and coarticulatory nasalized vowels in Canadian French) influenced spoken word recognition in an L2, as compared to a group of native (L1) listeners. Results from L2 listeners (English-native speakers) indicated that fine-grained phonetics impacted the recognition of words, i.e. they were able to use nasalization duration variability in a way similar to L1-French listeners, providing evidence that lexical representations can be highly specified in an L2. Specifically, L2 listeners were able to distinguish minimal word pairs (differentiated by the presence of phonological vowel nasalization in French) and were able to use variability in a way approximating L1-French listeners. Furthermore, the robustness of the French "nasal vowel" category in L2 listeners depended on age of exposure. Early bilinguals displayed greater sensitivity to some ambiguity in the stimuli than late bilinguals, suggesting that early bilinguals had greater sensitivity to small variations in the signal and thus better knowledge of the phonetic cue associated with phonological vowel nasalization in French, similarly to L1 listeners.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".