Language Contact Within the Speaker: Phonetic Variation and Crosslinguistic Influence
Bibliographic record
Abstract
A recent model of sound change posits that the direction of change is determined, at least in part, by the distribution of variation within speech communities. We explore this model in the context of bilingual speech, asking whether the less variable language constrains phonetic variation in the more variable language, using a corpus of spontaneous speech from early Cantonese-English bilinguals. As predicted, given the phonetic distributions of stop obstruents in Cantonese compared with English, intervocalic English /b d g/ were produced with less voicing for Cantonese-English bilinguals and word-final English /t k/ were more likely to be unreleased compared with spontaneous speech from two monolingual English control corpora. Whereas voicing initial obstruents can be gradient in Cantonese, the release of final obstruents is prohibited. Neither Cantonese-English bilingual initial voicing nor word-final stop release patterns were significantly impacted by language mode. These results provide evidence that the phonetic variation in crosslinguistically linked categories in bilingual speech is shaped by the distribution of phonetic variation within each language, thus suggesting a mechanistic account for why some segments are more susceptible to cross-language influence than others.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".