Achieving native-like accuracy of French/English vowels for Canadian bilingual speakers: effects of language self-described proficiency, context of use and daily exposure time
Bibliographic record
Abstract
Studies on bilingual production have not come to a consensus on the possibility of bilinguals reaching native-like production. Some studies find that bilinguals can be close to native production while others have shown that even in simultaneous bilinguals, the production cannot reach native-likeness. Flege’s Speech Learning Model (Flege & Bohn, 2020) theorizes that acquiring a second language affects the production of the L1 and vice versa. The model also states that new phonemes are easier to acquire compared to existing phonemes with different production. This thesis seeks to research the production of French and English vowels in a general population of Canadians to give an accurate picture of second language learning in Canada. We also seek to determine the strength of second language effects on the production of the first language and, what factors influence proficiency in bilingual speakers. Participants were recruited in the Montréal region to have bilinguals of different proficiency and different backgrounds. They were given a sentence list to read out loud while being recorded. The recorded data was used to study the vowel formants produced by the bilinguals. These formants were then used to create a plot of the group average that was then compared to the production plots of monolinguals. The production data was also used to create a group average distance, with the help of the Mahalanobis distance calculations, to also compare to the production from the French and English monolingual groups. The plots and the calculations were used to compare the groups between themselves and the monolingual groups. The results from the production data are within our expectations. The simultaneous bilinguals had the closest to native-like production in both English and French compared to the other bilinguals. The data also showed that for the English native bilinguals, the frequency of L2 use is the biggest factor in native-like production while for the French native bilinguals it was L2 proficiency. Regarding the SLM, the data we collected support the claim that bilinguals acquire and produce new phonemes with more ease than modify existing phonemes. It also partially supports the claim that knowledge of a second language will affect the production of the first language. The results from our experiment demonstrate that as the L1 French – L2 English bilinguals’ knowledge of English increased, their production of French veered away from the French monolinguals. However, this effect was not seen with the L1 English – L2 French bilinguals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".