Sonorant Onset Pitch as a Perceptual Cue of Lexical Tones in Mandarin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.062 | 0.003 |
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 it