Acoustic Characteristics of Adults’ Rhotic Monophthongs and Diphthongs
Bibliographic record
Abstract
Objectives: Rhotic sounds are known to be among the later developed sounds in young children, especially for those with speech sound disorders.Despite this fact, not many stu dies have examined the characteristics of rhotic vowels.Adults' productions of rhotic vow els have not been well investigated in spite of a relatively large literature on rhotic conso nants.This study examined the acoustic characteristics of rhotic monophthongs and diph thongs to see if certain phonetic contexts require less demanding articulatory movements or are different in vowel duration from other contexts, and thus work as a facilitating con text for the acquisition of rhotic vowels.Methods: Ten monolingual female adult speakers of Western Canadian English produced 36 target words containing two rhotic monoph thongs ([ɝ] and [ɚ]), and four rhotic diphthongs (/ɪ͡ ɚ/, /ɛ͡ ɚ/, /ɔ͡ ɚ/, and /ɑ͡ ɚ/) in both open and closed syllables.Acoustic analyses were performed to extract F2 and F3 values across the vowel duration, as well as the duration for each vowel.Results: Constantly low F3F2 val ues were found for rhotic monophthongs and rhotic diphthongs with front prerhotic vowels, but steeper downward movement was found for rhotic diphthongs with back pre rhotic vowels.Stressed and unstressed rhotic monophthongs showed similar acoustic pat terns, except for [ɝ] which was slightly longer than [ɚ].Across four rhotic diphthongs, no clear durational difference was found.Conclusion: Differences in acoustic patterns by pho netic contexts across six different rhotic vowel types suggest that certain phonetic con texts could provide more salient perceptual cues and thus facilitate relatively easier mas tery of sounds over others for young children.
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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) | 0.003 | 0.001 |
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".