A signal delection theory-based analysis of American English vowel identification and production performance by native speakers of Japanese
Bibliographic record
Abstract
The identification and production performance by two groups of native Japanese of the American English (AE) vowels /æ/, /a/, /■/, /■/, /■/ was measured before and after a six-week, identification training program. A signal detection theory (SDT) analysis of the confusion data, as measured by d′, revealed that all five AE vowels were more identifiable by the experimental trained group than the control untrained group. The d′ results showed that /■/ was less identifiable than /■/ in the pretest, even though the percentage identification rate for /■/ was slightly greater than that for /■/. Both groups productions of a list of CVCs, each containing one of the target AE vowels, were presented to a group of native AE listeners in a series of identification tasks. The d′ results revealed that the AE listeners could more sensitively identify the experimental groups post-test vowel productions than they could the control groups. SDT analysis also clarified an additional potentially confusing result: /■/ was somewhat less identifiable than /■/, despite the fact that the percentage identification rate for /■/ was higher. Overall, the SDT-based analysis served to change the pattern of results observed for L2 vowel identification and influenced the interpretation of the data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".