Perceptual categorization of English vowels by native European Portuguese speakers
Bibliographic record
Abstract
This study reports the results of a perceptual assimilation task (PAT) used to assess the degree of perceived cross-language (dis)similarity between the vowel inventories of European Portuguese (L1) and American English (L2) and, thus, predict difficulty in the perception and production of non-native vowels. Thirty-four native European Portuguese speakers completed a PAT, in which they mapped both L2 English and L1 Portuguese vowels to native vowel categories and rated them for goodness-of-fit to L1 vowels. The results are discussed in terms of theoretical models of cross-language perception and L2 speech learning (SLM, Flege, 1995, & PAM-L2, Best & Tyler, 2007).-----------------------------------------------------------------------------CATEGORIZAÇÃO PERCEPTIVA DE VOGAIS INGLESAS POR FALANTES NATIVOS DE PORTUGUÊS EUROPEUEste estudo reporta os resultados de uma tarefa de assimilação percetiva, usada para avaliar o grau de semelhança inter-linguística entre os inventários vocálicos de português europeu (L1) e de inglês americano (L2), e, assim, prever dificuldades na perceção e produção de sons não nativos. Trinta e quatro falantes nativos de português europeu completaram uma tarefa de assimilação perceptiva, na qual identificaram vogais do inglês (L2) e do português (L1) de acordo com as categorias fonológicas da sua língua nativa, avaliando também a qualidade de representatividade categorial. Os resultados são discutidos partindo de dois modelos de perceção inter-linguística e aprendizagem de fala L2 (SLM, Flege, 1995, & PAM-L2, Best & Tyler, 2007).---Original em inglês.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".