Categorization of speech sounds by Norwegian/English bilinguals
Bibliographic record
Abstract
Bilinguals who learned English late in life (late bilinguals) as opposed to those who learned English early in life (early bilinguals) differ in their perception of phonemic distinctions. Age of acquisition of a second language as well as depth of immersion into English is influenced by perceptual differences of phonemic contrasts between monolinguals and bilinguals, with consequences for speech production. The phonemes /v/ and /w/ are from the same category in Norwegian, rendering them perceptually indistinguishable to the native Norwegian listener. In English, /v/ and /w/ occupy two categories. Psychoacoustic testing on this phonemic distinction in the current study will compare perceptual abilities of monolingual English and bilingual Norwegian/English listeners. Preliminary data indicates that Norwegian/English bilinguals demonstrate varying perceptual abilities for this phonemic distinction. A series of speech sounds have been generated by an articulatory synthesizer, the Tube Resonance Model, along a continuum between the postures of /v/ and /w/. They will be presented binaurally over headphones in an anechoic chamber at a sound pressure level of 75 dB. Differences in the perception of the categorical boundary between /v/ and /w/ among English monolinguals and Norwegian/English bilinguals will be further delineated.
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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 | 0.001 |
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 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".