Phonetic category cues in adult-directed speech: Evidence from three languages with distinct vowel characteristics
Bibliographic record
Abstract
Using an artificial language learning manipulation, Maye, Werker, and Gerken (2002) demonstrated that infants’ speech sound categories change as a function of the distributional properties of the input. In a recent study, Werker et al. (2007) showed that Infant-directed Speech (IDS) input contains reliable acoustic cues that support distributional learning of language-specific vowel categories: English cues are spectral and durational; Japanese cues are exclusively durational. In the present study we extend these results in two ways. 1) we examine a language, Catalan, which distinguishes vowels solely on the basis of spectral differences, and 2) because infants learn from overheard adult speech as well as IDS (Oshima-Takane, 1988), we analyze Adult-directed Speech (ADS) in all three languages. Analyses revealed robust differences in the cues of each language, and demonstrated that these cues alone are sufficient to yield language-specific vowel categories. This demonstration of language-specific differences in the distribution of cues to phonetic category structure found in ADS provides additional evidence for the types of cues available to infants to guide their establishment of native phonetic categories.
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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 | 0.000 | 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".