The own-voice benefit for word recognition in early bilinguals
Bibliographic record
Abstract
The current study examines the self-voice benefit in an early bilingual population. Female Cantonese-English bilinguals produced words containing Cantonese contrasts. A subset of these minimal pairs was selected as stimuli for a perception task. Speakers' productions were grouped according to how acoustically contrastive their pronunciation of each minimal pair was and these groupings were used to design personalized experiments for each participant, featuring their own voice and the voices of others' similarly-contrastive tokens. The perception task was a two-alternative forced-choice word identification paradigm in which participants heard isolated Cantonese words, which had undergone synthesis to mask the original talker identity. Listeners were more accurate in recognizing minimal pairs produced in their own (disguised) voice than recognizing the realizations of speakers who maintain similar degrees of phonetic contrast for the same minimal pairs. Generally, individuals with larger phonetic contrasts were also more accurate in word identification for self and other voices overall. These results provide evidence for an own-voice benefit for early bilinguals. These results suggest that the phonetic distributions that undergird phonological contrasts are heavily shaped by one's own phonetic realizations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".