Leveraging audiovisual speech perception to measure anticipatory coarticulation
Bibliographic record
Abstract
A noninvasive method for accurately measuring anticipatory coarticulation at experimentally defined temporal locations is introduced. The method leverages work in audiovisual (AV) speech perception to provide a synthetic and robust measure that can be used to inform psycholinguistic theory. In this validation study, speakers were audio-video recorded while producing simple subject-verb-object sentences with contrasting object noun rhymes. Coarticulatory resistance of target noun onsets was manipulated as was metrical context for the determiner that modified the noun. Individual sentences were then gated from the verb to sentence end at segmental landmarks. These stimuli were presented to perceivers who were tasked with guessing the sentence-final rhyme. An audio-only condition was included to estimate the contribution of visual information to perceivers' performance. Findings were that perceivers accurately identified rhymes earlier in the AV condition than in the audio-only condition (i.e., at determiner onset vs determiner vowel). Effects of coarticulatory resistance and metrical context were similar across conditions and consistent with previous work on coarticulation. These findings were further validated with acoustic measurement of the determiner vowel and a cumulative video-based measure of perioral movement. Overall, gated AV speech perception can be used to test specific hypotheses regarding coarticulatory scope and strength in running speech.
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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 | 0.000 |
| 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".