Timing of Perioral Muscle Suppression in Smiled Speech
Bibliographic record
Abstract
During speech production, temporally overlapping movements can come into conflict. How such conflicts are resolved remains poorly understood. For example, during smiled speech, the simultaneous activation of facial expression and speech-related lip movements can generate oppositions between zygomaticus major (ZM) and orbicularis oris (OO) muscles; ZM activation pulls the lips apart for the smile, while OO activation pulls the lips together for lip closure and rounding movements [Stavness et al., 2013, JSLHR]. Previous research suggests that this conflict is resolved by suppression of either the smile or the lip closure movement [Liu et al., 2020, ISSP]. However, the mechanism by which one or the other movement is selected for suppression, or by which this suppression takes place, remains unknown. The present study aims to characterize the timing of the interaction that leads to this suppression, as well as the onset and length of suppression of smile when bilabial tokens are produced. All participants in this study were native English speakers between the ages of 18-25, instructed to read sentences featuring tokens (/m, f, v, b, p, w/) in three different postural conditions: neutral, smiling, and laughing. To measure the muscular “tug-of-war” between ZM and OO, electromyography (EMG) sensors were placed on the respective muscles, while simultaneous video recordings were processed using OpenFace 2.0 [Baltrušaitis et al., 2018, IEEE] to show corresponding facial action units of “lip corner puller” and “lip tightener” for each condition. Results and implications of these analyses will be discussed. OpenFace, a facial movement tracking tool, is used in this study to corroborate EMG measurements; as such, our results also provide evidence for the effectiveness of movement-based facial action units in reflecting muscle activity.
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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 | 0.003 |
| 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) | 0.002 | 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".