Orthographic influence in the distributional learning of non-native speech sounds
Bibliographic record
Abstract
This study investigated the role of orthographic information in the acquisition of non-native speech sounds by monolingual English listeners. Two potentially important orthographic variables were explored: Orthographic compatibility (whether the orthographic information supports or contradicts the distributional information) and orthographic familiarity (whether the native and target languages share the same orthography). Ten groups of learners were trained on either a unimodal or bimodal distribution of two length continua. Out of the 10 groups, eight groups were also exposed to orthographic cues that varied in their compatibility with the distributional information (compatible vs. incompatible) and familiarity with the orthography of learners’ native language (Roman vs. Arabic). Following training, all participants performed an AX discrimination task to test their discrimination of the length contrast. The results revealed that, in general, the availability of either familiar or unfamiliar orthographic input which signaled the existence of a single length category significantly lowered learners’ discrimination of the length contrast regardless of the auditory distribution. Further, the exposure to orthographic input that supported a two-category length distinction enhanced the discrimination of the length contrast irrespective of the distribution. However, the most significant improvement occurred when both distributional information and familiar orthographic input were compatible. Overall, these findings indicate that orthographic input, regardless of its level of compatibility or familiarity, may influence the acquisition of non-native speech sounds.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".