Cross-linguistic evidence for the influence of native language prosody in infant speech segmentation.
Bibliographic record
Abstract
Research using artificial languages with English-learning infants has shown both prosodic and distributional cues are used for speech segmentation by 7 months. When these cues conflict, infants younger than 7 months rely on distributional cues while older infants rely on prosodic cues). In the present study we assessed the role of prosodic information in segmentation when it is not favorably aligned with distributional cues, to determine whether language-specific rhythmic biases guide segmentation as suggested by studies using natural speech. Two continuous streams of naturally produced syllables (English and French) were constructed using nines syllables that are permissible in both languages. Within each stream statistical cues were manipulated independently of language-appropriate stress cues. English- and French-learning 8-month-olds were familiarized with their native language stream and then present test probe strings to determine what syllable sequences were extracted from the stream. Probes were selected to assess the role of stress cues in segmentation. Findings show that English infants make use of a trochaic template; Canadian-French infants show a weaker and less focused reliance on stress cues to segment words from connected speech. These cross-linguistic differences reflect processing biases that may be set by language experience and/or elicited by speech input properties.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".