A bear called Baddington? Variability and contrast enhancement in accented infant-directed speech
Bibliographic record
Abstract
This work examines the realization of the English stop voicing contrast in read speech directed to infants (IDS) and adults (ADS), as well as in words in isolation, as produced by three groups of speakers: native speakers of Canadian English (where /b/ and /p/ differ in aspiration), native speakers of languages in which /b/ and /p/ differ in phonetic voicing (e.g., Spanish), and native speakers of languages which have a 4-way stop contrast /b, p, bʰ, pʰ/, where both aspiration and voicing are contrastive (e.g., Hindi). In words in isolation, speakers from both “accented” groups tended to produce English voiceless stops as unaspirated, and voiced stops as phonetically voiced. However, there was variability in accented speakers’ voiceless stops, as well as in native speakers’ use of phonetic voicing in voiced stops, and this variability appeared to be augmented in the read speech conditions (ADS and IDS). We test the hypotheses (1) that IDS results in phonologically-informed contrast enhancement, with accent-specific modifications expected for the three groups of speakers, and (2) that speakers aim for more precision in phonetic targets when talking to infants, resulting in less within-speaker variability in realization of the contrast in IDS as compared to ADS.
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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 | 0.000 | 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".