Vowel devoicing in Japanese infant- and adult-directed speech
Bibliographic record
Abstract
It is well known that parents make systematic changes in the way they speak to infants; they use higher pitch overall, more pronounced pitch contours, more extreme point vowels, and simplified morphology and syntax (Andruski and Kuhl, 1996; Fernald et al., 1989). Yet, they also preserve information crucial to the infants ability to acquire the phonology of the native language (e.g., phonemic length information, Werker et al., 2006). The question examined in this paper is whether information other than phonemic segmental information is also preserved, namely, information concerning the phonological process of vowel devoicing. Devoicing of high vowels between voiceless consonants and word-finally after a voiceless consonant is a regular and well-attested phonological process in Japanese (Shibatani, 1990). A corpus of speech by Japanese mothers addressed to their infants and addressed to another adult was examined, and the degree and frequency with which they apply vowel devoicing in each type of speech was analyzed. Rates of vowel devoicing in speech to adults and infants are compared, accommodations made to infants and to hearing-impaired children are discussed (Imaizumi et al., 1995), and the implications of these accommodations for the acquisition of vowel devoicing by Japanese infants are explored.
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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 | 0.000 |
| 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".