Cross-language identification of non-native lexical tone
Bibliographic record
Abstract
We extend to lexical-tone systems a model of second-language perception, the Perceptual Assimilation Model (PAM) (Best & Tyler, 2007), to examine whether native-language lexical-tone experience influences identification of novel tone. Native listeners of Cantonese, Thai, Mandarin, and Yoruba hear six CV syllables, each produced with the three phonemic Yoruba tones (High-level/H, Mid-level/M, Low-level/L), presented randomly three times. In a 3-AFC task, participants indicate a syllable’s tone by selecting from a set of arrows the one that illustrates its pitch trajectory. Accuracy scores (proportion correct) were submitted to a two-way rANOVA with L1-Group (x4) as the between-subjects factor and Tone (x3) as the within-subjects factor. There was no main effect of Tone or Group. The Tone-by-Group interaction was significant (p = 0.031) but driven by one group: Thai listeners identified H and M more accurately than L (both p < 0.05), though L accuracy was above chance (59%; chance = 33.33%). Tone-error patterns indicate that Thai listeners primarily confused L with M (two-way L1-Group x Response-pattern rANOVA p < 0.05). Overall, despite their different tonal-L1 backgrounds, listeners performed comparably. As predicted by the PAM, listeners attended to gradient phonetic detail and acoustic cues relevant to L1 phoneme distinctions (F0 height/direction) in order to classify non-native contrasts. [NSF grant #0965227.]
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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 | 0.004 |
| 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) | 0.003 | 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".