Prosodic location modulates listener’s perception of novel German sounds
Bibliographic record
Abstract
Interaction of sounds on the melodic tier (segments) with prosodic and phonotactic structure (syllabic context) in cross-language perception is not explicitly addressed by models of second language phonology (e.g., Perceptual Assimilation Model: Best, 1995). At initial stages of foreign language exposure, learners rely on position-specific phonetic detail more than native speakers or advanced learners, thus mappings according to prosodic and phonotactic context are a crucial factor in early interlanguage phonological development. In a perceptual assimilation experiment, we manipulated syllable position (onset vs. coda) and phonotactic complexity (simple vs. complex codas) in phonotactically similar languages for auditory presentation of six German obstruents (i.e., familiar [h k ʃ] and novel [ç x p͡f]) to native speakers of American English who had no previous exposure to German. By means of weighted proportions (Park & de Jong, 2008) and overlap scores (Levy, 2009), we found that [h k ʃ p͡f] mapped categorically to English orthographic categories , , , and , respectively, in all positions, whereas the novel fricatives [ç x] exhibited distinct mapping patterns from each other, from other sounds, and according to syllable position. These results demonstrate profound influences of both low-level prosodic and phonotactic contexts on perceptual assimilation of novel 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".