Incongruence in second language vowel perception and production
Bibliographic record
Abstract
Nearey’s work on double-weak theory has brought to the fore the conceptualization of native-language (L1) speech perception and production as autonomous subsystems. In second-language (L2) phonetics research, the perception-production relationship is also a central concern, although the facilitating and hampering effects of a previously learned phonological inventory must be taken into account (Flege, 2003). Some current views of L2 phonetic learning assume that a reorganization of perceptual knowledge normally comes first, and that production eventually falls into line with perceptual representations (Huensch, 2013; Thomson, 2011, 2013). However, the available data point to a number of complexities in the relationship that have yet to be accounted for. In the present report, we synthesize new data on vowel perception and production by English learners from a variety of L1 backgrounds. Among the key findings are that (1) perceptual training on vowels in particular phonetic contexts leads to very limited transfer of learning to new phonetic contexts, (2) perceptual accuracy only weakly predicts production accuracy, especially for cross-linguistically marked vowels, (3) word familiarity and frequency correlate with vowel production accuracy, and (4) elicitation techniques influence production accuracy. These issues are discussed in terms of the problems they pose for models of phonetic learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".