The perceptual assimilation model for suprasegmentals and cross-language lexical-tone identification
Bibliographic record
Abstract
We examine how native lexical-tone experience influences identification of novel tone. Cantonese, Thai, Mandarin, and Yoruba listeners identified CV syllables bearing the six phonemic Cantonese tones. Accuracy scores were submitted to a two-way rANOVA with L1-Group (x4) as the between-subjects factor and Tone (x6) as the within-subjects factor. Tone error patterns were also assessed via rANOVAs with L1-Group (x4) as the between-subjects factor and Response-Pattern (% correct versus % other response) as the within-subjects factor. Consistent with previous reports, native listeners’ confusions reflected effects of ongoing tonal mergers and a crowded tone space. Non-native listeners appeared to assimilate novel tones to L1 tone categories by attending to phonetic cues relevant to the phonological and phonetic properties of their L1s. Overall, results support predictions of the Perceptual Assimilation Model for Suprasegmentals (PAM-S). [Support: NSF grant 0965227 to J.A.A.]
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".