Bidirectional C-to-V coarticulation across syllable and word boundaries
Bibliographic record
Abstract
Extensive work has explored the anticipatory or carryover coarticulatory effects of consonants on F2 of adjacent vowels using locus equation. However, studies examining bidirectional effects remain scarce. This study examines how voicing and place of articulation (PoA) of consonants affect bidirectional C-to-V coarticulation across syllable and word boundaries. Recordings of / C;1V;1.C;2V;2/, /C;1V;1#C;2V;2/ and /C;1V;1C;2#V;2t/ sequences from native speakers of Canadian English were analyzed. F0, F1, and F2 were measured at the onset and offset of the target vowels (/i ɑ/) to compare the coarticulatory effects caused by the adjacent consonants with different voicing and PoA (/p b t d g k/). Preliminary results (N = 6) indicate that coarticulatory effects vary based on voicing and PoA of consonants. Voiceless consonants exhibited a greater effect on f0 bidirectionally, but smaller effects on F1 and F2. Regarding PoA, only the effect of F2 in anticipatory contexts was dependent on different PoA. The findings also suggest that bidirectional coarticulatory effects are found across both syllable and word boundaries. Within and across these boundaries, larger anticipatory effects were found on f0, while the carryover effect on F1 was more robust. Concerning F2, differences between coarticulatory effects were only evident in voiceless and velar contexts.
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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 | 0.001 | 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".