The roles of tone and syllable structure in Mandarin spoken word recognition.
Bibliographic record
Abstract
The present study adopts the gating paradigm to investigate the roles of tone, onset sonorancy, and nasal coda in Mandarin spoken word recognition. Duration-blocked gates generated from eight monosyllabic quadruplets with matching frequencies of occurrence were used as stimuli. The initial consonant of each syllable formed the first gate, with later gates formed by 40 ms increments. Twenty-eight native Mandarin speakers from Beijing were asked to identify each gated stimulus by writing down the Chinese characters. Isolation point (IP) based on correct tone identification as well as overall correct word identification (correct onset, rhyme, and tone) were collected. Results from both conditions showed that tone 1 has an earlier IP than tone 4, which has an earlier IP than tones 2 and 3. Sonorant-initial syllables have an earlier IP than obstruent-initial syllables, but further analyses of covariance indicated that this is due to the fact that IP covariates with the duration of the initial consonant. Syllables without a nasal coda have an earlier IP than syllables with a nasal coda. This effect might be due to the interference of nasalization on tone perception or the delayed tonal contour realization due to the nasal coda [Xu, (1998)].
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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 | 0.003 |
| 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 | 0.000 |
| 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".