Acoustic and articulatory qualities of smiled speech
Bibliographic record
Abstract
Studies on smiled speech have shown that listeners can easily identify speech that was produced while based solely on the acoustic signal (c.f. Tartter, 1980; Quene, Semin, & Foroni 2012; Quene & Schuerman 2012; Torre, 2014). In general, these studies have primarily focused on the acoustic and perceptual effects of on speech; surprisingly little work has been done on the ways in which while talking affects speech articulations, and how those articulatory changes map onto the acoustic differences. The current study aims to address this gap through a production experiment examining both the articulation and acoustics of vowels in smiled versus non-smiled speech. The experiment examined the effect of on formant values, lip spreading, lip protrusion, lip angle, and larynx height in the production of vowels by 10 native English speakers. Facial movement and positioning were measured following Fagel (2010), using dots on participants’ faces, and larynx height was measured with laryngeal ultrasound following Moisik and colleagues (Moisik, Esling, Bird, & Lin 2011; Moisik & Esling, 2011; Moisik, Lin, & Esling, 2014). We hypothesized that smiled speech, in comparison to neutral speech, would be characterised by a higher F0, higher formant frequencies, a raised larynx, and spread lips with corners turned up in a typical smiling configuration. Preliminary results show that is indeed characterized by higher F0 and lips spread with corners turned up. However, formant frequencies were only significantly different in smiled speech for /?/, not for /u/ or /i/. Further, larynx height was not significantly different between smiled and neutral speech, despite the differences in F0. Implications to the theory of smiled speech will be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".