Cross-language Perception of Non-native Tonal Contrasts: Effects of Native Phonological and Phonetic Influences
Bibliographic record
Abstract
This study examined the perception of the four Mandarin lexical tones by Mandarin-naïve Hong Kong Cantonese, Japanese, and Canadian English listener groups. Their performance on an identification task, following a brief familiarization task, was analyzed in terms of tonal sensitivities (A-prime scores on correct identifications) and tonal errors (confusions). The A-prime results revealed that the English listeners' sensitivity to Tone 4 identifications specifically was significantly lower than that of the other two groups. The analysis of tonal errors revealed that all listener groups showed perceptual confusion of tone pairs with similar phonetic features (T1-T2, T1-T4 and T2-T3 pairs), but not of those with completely dissimilar features (T1-T3, T2-T4, and T3-T4). Language-specific errors were also observed in their performance, which may be explained within the framework of the Perceptual Assimilation Model (PAM: Best, 1995; Best & Tyler, 2007). The findings imply that linguistic experience with native tones does not necessarily facilitate non-native tone perception. Rather, the phonemic status and the phonetic features (similarities or dissimilarities) between the tonal systems of the target language and the listeners' native languages play critical roles in the perception of non-native tones.
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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) | 0.000 | 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 | 0.000 |
| 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".