Prioritizing Pronunciation Features for Intelligibility: A Perception Study of the French /E/ Vowels
Bibliographic record
Abstract
Despite the fact that pronunciation is a vital aspect of language learning, previous research has shown that its instruction is limited in the classroom in terms of time and attention, due in part to the time constraints inherent in any curriculum. It then becomes essential for instructors to be strategic about the pronunciation features they explicitly teach. The features that most affect the intelligibility of the learners must be identified through research for each target language. The present study aims to determine whether the commonly taught French mid-vowel pair /e/~/ɛ/ should be prioritized in the language classroom by identifying both the extent to which its pronunciation by intermediate- and advanced-level learners can be distinguished by native listeners and which phonological contexts most affect lexical intelligibility. Results indicate that the learners were nearly as intelligible as a native-speaker control group with regard to target vowel production, suggesting that the /e/~/ɛ/ pair should not be a priority at those levels of instruction. Additionally, identification rates decreased when the target vowels occurred in word-initial and open-syllable positions, which patterns after trends in the speech of native speakers, as observed in other studies.
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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 | 0.003 |
| 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 | 0.000 |
| 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".