Sibilant Fricative Merging in Taiwan Mandarin: An Investigation of Tongue Postures using Ultrasound Imaging
Bibliographic record
Abstract
In Taiwan Mandarin, retroflex [ʂ] is allegedly merging with dental [s], reducing the traditional three-way contrast between sibilant fricatives (i.e., dental [s]–retroflex [ʂ]–alveopalatal [ɕ]) to a two-way contrast. Most of the literature on the observed merging focuses on the acoustic properties and perceptual identification of the sibilants, whereas much less attention has been drawn to the articulatory evidence accounting for the aforementioned sibilant merging. The current study employed ultrasound imaging techniques to uncover the tongue postures for the three sibilant fricatives [s, ʂ, ɕ] in Taiwan Mandarin occurring before vowels [a], [ɨ], and [o]. Results revealed varying classes of the [s–ʂ] merger: complete merging ( overlap), no merging ( non-overlap), and context-dependent merging ( context-dependent overlap, which only occurred before [a]). The observed [s–ʂ] merger was also confirmed by the perceptual identification by trained phoneticians. Center of gravity (CoG), a reliable spectral moment of identifying different sibilant fricatives, was also measured to reflect the articulatory–acoustic correspondence. Results showed that the [s–ʂ] merger varies across speakers and may also be conditioned by vowel contexts and that articulatory mergers may not be entirely reflected in CoG values, suggesting that auxiliary articulatory gestures may be employed to maintain the acoustic contrast.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".