Sonorant Onset Pitch as a Perceptual Cue of Lexical Tones in Mandarin
Bibliographic record
Abstract
Lexical tone identification requires a number of secondary cues, when main tonal contours are unavailable. In this article, we examine Mandarin native speakers' ability to identify lexical tones by extracting tonal information from sonorant onset pitch (onset contours) on syllable-initial nasals ranging from 50 to 70 ms in duration. In experiments I and II we test speakers' ability to identify lexical tones in a second syllable with and without onset contours in isolation (experiment I) and in a sentential context (experiment II). The results indicate that speakers can identify lexical tones with short distinctive onset contour patterns,they also indicate that misperception of tones 213 and 24 are common. Furthermore, in experiment III, we test whether onset contours in a following syllable can be utilized by listeners in tone identification. We find that onset contours in the following syllable also contribute to the identification of the target lexical tones. The conclusions are twofold: (1) Mandarin lexical tones can be identified with onset contours; (2) tonal domain must be extended to include not just typical cues of tones but also coarticulated tonal patterns.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".