Modeling retroflex fricative variation in accented mandarin
Bibliographic record
Abstract
Since dental-retroflex fricative contrast is not consistently maintained in many southern dialects of Chinese, native speakers of these dialects may not accurately produce the Mandarin retroflex fricative /ʂ/. Consequently, /ʂa/ may be realized as [sa] (Duanmu, 2007). This study investigated the variation of the retroflex fricative /ʂ/ in a Chinese Mandarin speech corpus (DataTang, 2018). The corpus contains 200 hours of recordings of 600 speakers from different dialectal regions in China. Each recording was aligned at the phone level using Montreal Forced Aligner. The center of gravity of the acoustic energy (COG) of the target sounds was extracted using Christian DiCanio’s Praat script. For statistical analysis, the generalized additive mixed-effects model (GAMM) was used. COG was the response variable. The following vowel’s height, tone, and gender were factorial predictors. To evaluate the geographic effect, we used tensor product smooths by fricatives with the longitude and latitude of each speaker’s birthplace (Chuang et al., 2021). Our results suggested a more dental-like realization of the retroflex for speakers from southern China and significant effects of the following vowel’s height and gender in the realization of Mandarin retroflex fricative.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".