Sound Change in Spontaneous Bilingual Speech: A Corpus Study on the Cantonese n-l Merger in Cantonese-English Bilinguals
Bibliographic record
Abstract
In Cantonese and several other Chinese languages, /n/ is merging with /l/.The Cantonese merger appears categorical, with /n/ becoming /l/ word-initially.This project aims to describe the status of /n/ and /l/ in bilingual Cantonese and English speech to better understand individual differences at the interface of crosslinguistic influence and sound change.We examine bilingual speech using the SpiCE corpus, composed of speech from 34 early Cantonese-English bilinguals.Acoustic measures were collected on pre-vocalic nasal and lateral onsets in both languages.If bilinguals maintain separate representations for corresponding segments across languages, smaller differences between /n/ and /l/ are predicted in Cantonese compared to English.Measures of mid-frequency spectral tilt suggest that the /n/ and /l/ contrast is robustly maintained in English, but not Cantonese.The spacing of F2-F1 suggests small differences between Cantonese /n/ and /l/, and robust differences in English.While cross-language categories appear independent, substantial individual differences exist in the data.These data contribute to the understanding of the /n/ and /l/ merger in Cantonese and other Chinese languages, in addition to providing empirical and theoretical insights into crosslinguistic influence in early 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".