Spoken Word Recognition in Native and Second Language Canadian French: Phonetic Detail and Representation of Vowel Nasalization
Bibliographic record
Abstract
Research has shown that fine-grained consonantal phonetic information can be gradiently integrated during spoken word recognition in the L1. However, the way listeners categorize vocalic phonetic information has not been investigated as thoroughly. Furthermore, second language (L2) listeners’ processing of fine-grained information is not as well known as L1 processing. L1 Canadian French (CF) listeners and L2 listeners (native English) were tested in an eye tracking paradigm with words containing partially nasalized (CVN) and fully nasal (CṼ) vowels. Stimuli were designed to have variable nasalization duration on the vowel, and sometimes include a short nasal consonant word-finally. The main goals were to determine how nasalization duration influences word recognition in an L1 and an L2, and if variations in phonetic details are gradiently or categorically integrated. Results show that L1 listeners gradiently were able to identify the stimuli when they contained mismatching phonetic cues, while L2 listeners display more categorical patterns of recognition. When stimuli do not have conflicting phonetic cues, L1 listeners mostly identify words as CṼ, except when the vowel is not nasalized. For L2 listeners, the pattern was similar, but the rate of stimuli identification as phonological nasal (CṼ) was lower due to L1 transfer. These results support the hypothesis that L1 listeners have phonological representations that include fine-grained phonetic information and that they consider it when recognizing words. On the other hand, L2 listeners who have less experience in the L2 display more categorical recognition patterns, probably because their representations include coarser phonetic information or because they cannot access fine-grained representations, given the cognitive demands of L2 processing. When words do not contain conflicting phonetic cues, patterns of recognition of both L1 and L2 listeners seem more categorical, even though L2 listeners displayed lower rates of identification than L1 listeners overall. This uncertainty can also be due to less detailed phonological representations or to their inability to access all the necessary information to recognize words. Overall, these results suggest that fine-grained phonetic information gradiently impacts word recognition, that it is part of phono-lexical representations, and that L2 processing is qualitatively and quantitatively different from L1 processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".