An ultrasound study of the acquisition of North American English /ɹ/
Bibliographic record
Abstract
I report an acoustic and articulatory study of North American English /ɹ/ production in typically developing English-speaking children during early- and later-stage acquisition. North American English /ɹ/ is of interest in adult populations because it exhibits acoustic stability (e.g. low F3) despite considerable articulatory variability both within and between speakers (Delattre and Freeman, 1968). North American English /ɹ/ is also often one of the last sounds to be acquired by children (Smit, 1993; Schriberg, 1993), especially in prevocalic position (Smit et al. 1990; McGowan et al. 2004). Tiede et al. (2011) have argued that children might attempt different vocal tract configurations during acquisition, particularly in contexts where the articulatory demands are greater. While there is a growing body of literature on articulatory variability in adult production of /ɹ/ (e.g. Mielke et al. 2010, Campbell et al. 2011), there remains virtually no articulatory data on typically developing children's production during acquisition. This study uses ultrasound imaging to investigate the articulations of four typically developing English-speaking children, aged between 3 and 6 years, during production of familiar lexical items. Children's early-stage articulations are examined and compared with their later-stage productions, and with adult variability patterns.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".