The Acoustics of Borrowed /ɚ/ in Quebec French
Bibliographic record
Abstract
The English rhotic vowel /ɚ/ (as in soccer, /sɑkɚ/), characterized by a low third formant, is a cross-linguistically rare sound. Quebec French lacks such a phoneme, which poses a problem when borrowing words from English which contain it. Languages are known to employ a wide array of strategies in such cases, with varying degrees of acoustic fidelity to the original form. Quebec French is said to use three of these to adapt English /ɚ/: ignoring the pronunciation from the donor language and instead deducing one from the orthography (e.g., [sɔkɛʁ]), substituting the (phonetically or phonologically) closest native phoneme (usually rendered as [sɔkœʁ]), and importing the missing sound wholesale (i.e., [sɔkɚ]) (cf. Côté 2021). However, the acoustics of this loanword vowel have not yet been systematically studied. This paper constitutes a pilot study aimed at addressing this gap. The data consist of 317 tokens of the word Orford (a place name of English origin) across 26 speakers, taken from a corpus of parliamentary speech (an expanded version of Milne’s 2014 AssNat corpus). Static and dynamic formant measures for the vowel of interest are reported. In addition, overlap in F1 × F2 × F3 distributions with the acoustically similar native phonemes /ø, œ/ is calculated for each speaker using Pillai scores (cf. Hay et al. 2006, Nycz & Hall-Lew 2014). Preliminary results evidence a large measure of intra- and inter-speaker variability, particularly in terms of F3 (indicating variable success in replicating the rhoticity of the English phoneme). For most speakers, the loanword vowel remains distinct from either of the acoustically comparable native phonemes—even amongst those who exhibit similar F3 values as for /ø/ or /œ/—albeit with greater similarity to /ø/ than to /œ/. These findings paint a more complicated portrait of the variability in adaptation of /ɚ/ than earlier descriptions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".