The Edge Factor in Early Word Segmentation: Utterance-Level Prosody Enables Word Form Extraction by 6-Month-Olds
Bibliographic record
Abstract
Past research has shown that English learners begin segmenting words from speech by 7.5 months of age. However, more recent research has begun to show that, in some situations, infants may exhibit rudimentary segmentation capabilities at an earlier age. Here, we report on four perceptual experiments and a corpus analysis further investigating the initial emergence of segmentation capabilities. In Experiments 1 and 2, 6-month-olds were familiarized with passages containing target words located either utterance medially or at utterance edges. Only those infants familiarized with passages containing target words aligned with utterance edges exhibited evidence of segmentation. In Experiments 3 and 4, 6-month-olds recognized familiarized words when they were presented in a new acoustically distinct voice (male rather than female), but not when they were presented in a phonologically altered manner (missing the initial segment). Finally, we report corpus analyses examining how often different word types occur at utterance boundaries in different registers. Our findings suggest that edge-aligned words likely play a key role in infants' early segmentation attempts, and also converge with recent reports suggesting that 6-month-olds' have already started building a rudimentary lexicon.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".