Hypoarticulation as a tool for assessing social distance: an acoustic study of speech addressed to different types of interlocutors
Bibliographic record
Abstract
Work within Hyper-Hypoarticulation Theory (H&H) and Communication Accommodation Theory (CAT) is increasingly focused on the adaptation of speech to the identity of the interlocutor (Koppen et al. 2017, Pardo et al. 2012, among others). These studies show a correlation between changes in the rate and spectral characteristics of speech (especially vowels) and the relationship between the speakers. Using the Diapix task (Baker & Hazan 2011), 10 Québec-French-speaking couples were invited to interact together and with two strangers, one French and one Québécois. This produced a corpus of 25h of speech and 121000 vowels. Spectral variations (especially hyper- / hypo- articulation), and changes in speech rate depending on the interlocutor, were studied using ((G)LMM) analysis. Our results reveal a correlation between the degree of social distance and speech reduction: the closer the interlocutors are (partners), the more speech is reduced.
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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".