Cantonese and Japanese listeners’ processing of Russian onset and coda stops
Bibliographic record
Abstract
The study investigated influences of native phonological and phonetic knowledge on Cantonese and Japanese listeners’ processing of non-native stop consonants. Phonologically, Cantonese Chinese has a three-way place contrast in stops in onset and coda positions; Japanese has a similar contrast, however, only in onset position. Phonetically, Cantonese coda stops are acoustically unreleased; Japanese stops followed by devoiced vowels are acoustically similar to released coda stops in other languages, such as Russian. Two groups of listeners, native speakers of Hong-Kong Cantonese and Japanese, were presented with sequences of Russian voiceless stops (VC1♯C2V, where C1/C2=/p/, /t/, or /k/). Two tasks were employed: (i) identification of onset or coda stops and (ii) discrimination of sequences that differed in either onset or coda consonant. Both groups performed equally well in the identification and discrimination of onset stops. However, Japanese listeners performed significantly better than Cantonese listeners in the identification and discrimination of coda stops. The findings suggest that in non-native listening, the low-level native phonetic knowledge—the acoustic realization of stop place contrasts—can override the higher-level phonological knowledge—syllable structure constraints on the distribution of place features. [Work supported by Social Sciences and Humanities Research Council of Canada.]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".