Multidimensional category-goodness-difference assimilation of Canadian English /i/ and /ɪ/ to Spanish /i/
Bibliographic record
Abstract
Morrison [J. Acoust. Soc. Am., 117, 2401 (2005)] theorized that L1-Spanish learners of English initially identify the Canadian English /i/-/■/ contrast via a multidimensional category-goodness-difference assimilation to Spanish /i/, with more-Spanish-/i/-like vowels (shorter vowels, lower F1 and higher F2) labelled as English /■/ and less-Spanish-/i/-like vowels (longer vowels, higher F1 and lower F2) labelled as English /i/. The L1-Spanish listeners’ use of duration is positively correlated with L1-English speakers’ productions, but their use of spectral cues is negatively correlated; hence, exposure to English results in increased use of duration cues and reduced use of spectral cues, leading to unidimensional duration-based perception. Since, in L1-English speakers’ productions, spectral properties are partially correlated with duration properties, L2-English learners can use duration-based perception as a bootstrap for learning L1-English-like spectral-based perception. Results from a new study are presented. Unlike the previous study, synthetic stimuli in the new study included vowel inherent spectral change (VISC). Results were consistent with the theory above: Vowels that were poor matches for Spanish /i/ because they had converging-VISC were labelled as English /i/, and vowels with no VISC were labelled as English /■/. [Work supported by SSHRC.]
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".