Bibliographic record
Abstract
The goal of this study was to identify acoustic parameters associated with the expression of sarcasm by Cantonese speakers, and to compare the observed features to similar data on English [Cheang, H. S. and Pell, M. D. (2008). Speech Commun. 50, 366-381]. Six native Cantonese speakers produced utterances to express sarcasm, humorous irony, sincerity, and neutrality. Each utterance was analyzed to determine the mean fundamental frequency (F0), F0-range, mean amplitude, amplitude-range, speech rate, and harmonics-to-noise ratio (HNR) (to probe voice quality changes). Results showed that sarcastic utterances in Cantonese were produced with an elevated mean F0, and reductions in amplitude- and F0-range, which differentiated them most from sincere utterances. Sarcasm was also spoken with a slower speech rate and a higher HNR (i.e., less vocal noise) than the other attitudes in certain linguistic contexts. Direct Cantonese-English comparisons revealed one major distinction in the acoustic pattern for communicating sarcasm across the two languages: Cantonese speakers raised mean F0 to mark sarcasm, whereas English speakers lowered mean F0 in this context. These findings emphasize that prosody is instrumental for marking non-literal intentions in speech such as sarcasm in Cantonese as well as in other languages. However, the specific acoustic conventions for communicating sarcasm seem to vary among languages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".