Differences in the development of speech segmentation abilities in two French dialects
Bibliographic record
Abstract
Speech segmentation skills develop in infancy and are influenced by many phonological properties of the native language and, in particular, the prosodic structure of the infant's native language. Studies using the HPP task show that American English infants appear to favor a stress-based procedure (Jusczyk et al., 1999) whereas Parisian French infants favor a syllable-based procedure (Nazzi et al., 2006), in line with a prosodic-based bootstrapping account of segmentation abilities (Nazzi et al., 1998). However, in a study using different stimuli, Polka and Sundara (2003) found results that might suggest a developmental trajectory for Canadian French infants that does not rely on syllable-based segmentation. Given that the stimuli used in both studies on French were different, both research teams tested their infant populations (at 8, 12, and 16 months of age) with the stimuli originally used by the other team The results suggest a complex interaction between specific stimuli and infants' native dialect, thus providing for the first time data suggesting that word segmentation is dialect specific. We will discuss how these findings, together with acoustic analyses, show that early word segmentation is influenced by linguistic (rhythmic/syllabic, dialectal) prosodic cues, extra-linguistic (speech style) prosodic cues, and distributional cues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".