Cross-Linguistic Phonetic Variation in Bilingual Speech: Cantonese /n/ > [l] Merger in Early Cantonese–English Bilinguals
Bibliographic record
Abstract
/n/ is merging with /l/ in Cantonese, as well as in several other Chinese languages. The Cantonese merger appears categorical, with /n/ becoming /l/ syllable-initially. This project aims to describe /n/ and /l/ in Cantonese and English speech from early Cantonese–English bilinguals to better understand the status of the merger in Cantonese and its potential for cross-linguistic mutual influence. We examine early bilinguals’ ( n = 34) speech using the Speech in Cantonese and English (SpiCE) corpus, focusing on pre-vocalic /n/ and /l/ onsets in both languages. Items were auditorily coded for their perceived category identity, and two acoustic measures anticipated to have the potential to differentiate /n/ and /l/ within and across languages were applied. In English, bilinguals maintained a clear contrast between /n/ and /l/ in the auditory coding and in acoustic measurements. In Cantonese, however, there were higher rates of [l] for /n/ items, in line with the merger, and [n] for /l/ items, indicating hypercorrection of the pattern. Across languages, bilinguals produced language-specific /l/s, but there were no acoustic differences between Cantonese and English /n/. The participation of Cantonese /n/ in a sound change does not appear to compromise English /n/s’ patterning, suggesting that Cantonese and English /n/ are maintained as distinct categories in the minds of early bilinguals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".