The dual role of post-stop fundamental frequency in the production and perception of stops in Mandarin-English bilinguals
Bibliographic record
Abstract
In non-tonal languages with a two-way laryngeal contrast, post-stop fundamental frequency (F0) tends to vary as a function of phonological voicing in stops, and listeners use it as a cue for stop voicing. In tonal languages, F0 is the most important acoustic correlate for tone, and listeners likewise rely heavily on F0 to differentiate tones. Given this ambiguity of F0 in its ability to signal phonological voicing and tone, how do speakers of a tonal language weight it in production and perception? Relatedly, do bilingual speakers of tonal and non-tonal languages use the same weights across different language contexts? To address these questions, the cross-linguistic performances from L1 (first language) Mandarin-L2 (second language) English bilinguals dominant in Mandarin in online production and perception experiments are compared. In the production experiment, the participant read aloud Mandarin and English monosyllabic words, the onsets of which typified their two-way laryngeal contrast. For the perception experiment, which utilized a forced-choice identification paradigm, both the English and Mandarin versions shared the same target audio stimuli, comprising monosyllables whose F0 contours were modeled after Mandarin Tone 1 and Tone 4, and whose onset was always a bilabial stop. The voice onset time of the bilabial stop and the onset F0 of the nucleus were manipulated orthogonally. The production results suggest that post-stop F0 following aspirated/voiceless stops was higher than that following unaspirated/voiced stops in both Mandarin and English production. However, the F0 difference in English was larger as compared to Mandarin, indicating that participants assigned more production weight to post-stop F0 in English than in Mandarin. On the perception side, participants used post-stop F0 as a cue in perceiving stops in both English and Mandarin, with higher post-stop F0 leading to more aspirated/voiceless responses, but they allocated more weight to post-stop F0 when interpreting audio stimuli as English words than as Mandarin words. Overall, these results argue for a dual function of F0 in cueing phonological voicing in stops and lexical tone across production and perception in Mandarin. Furthermore, they suggest that bilinguals are able to dynamically adjust even a secondary cue according to different language contexts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".