Target spectral, dynamic spectral, and duration cues in infant perception of German vowels
Bibliographic record
Abstract
Previous studies of vowel perception have shown that adult speakers of American English and of North German identify native vowels by exploiting at least three types of acoustic information contained in consonant-vowel-consonant (CVC) syllables: target spectral information reflecting the articulatory target of the vowel, dynamic spectral information reflecting CV- and -VC coarticulation, and duration information. The present study examined the contribution of each of these three types of information to vowel perception in prelingual infants and adults using a discrimination task. Experiment 1 examined German adults' discrimination of four German vowel contrasts (see text), originally produced in /dVt/ syllables, in eight experimental conditions in which the type of vowel information was manipulated. Experiment 2 examined German-learning infants' discrimination of the same vowel contrasts using a comparable procedure. The results show that German adults and German-learning infants appear able to use either dynamic spectral information or target spectral information to discriminate contrasting vowels. With respect to duration information, the removal of this cue selectively affected the discriminability of two of the vowel contrasts for adults. However, for infants, removal of contrastive duration information had a larger effect on the discrimination of all contrasts tested.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".