Bilingual sibilant acoustics in conversational Cantonese-English speech
Bibliographic record
Abstract
Similar sibilants in different languages can differ in their trajectories, even when other cues fail to differentiate them [Reidy, J. Acoust. Soc. Am. 140, 2518 (2016)]. Whether bilinguals—whose languages influence each other in a multitude of ways—would maintain such a difference in sibilant production across their two languages remains an open question. Using the transcribed and force-aligned (female) half of a new corpus recorded in Vancouver, Canada during 2018–2019, this study compares word-initial, prevocalic Cantonese /s/ productions to English /s/ and /ʃ/ productions in the same environment, by the same set of talkers. Cantonese /s/ is of interest given its variable description in the literature, across measurements, talkers, and vowel environments [Yu, J. Acoust. Soc. Am. 139, 1672 (2016)]. This study addresses how bilinguals produce Cantonese /s/, and whether or not it is acoustically comparable to either of their English voiceless sibilants. Sibilant productions are measured in a variety of ways, for comparison with previous studies. This includes peak ERBN number trajectories, which capture spectro-temporal variation. All comparisons are within-talker. Cross-talker differences in language background are also considered.
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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 | 0.001 | 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".