The sounds of sarcasm in English and Cantonese : a cross-linguistic production and perception study
Bibliographic record
Abstract
Three studies were conducted to examine the acoustic markers of sarcasm in English and in Cantonese, and the manner in which such markers are perceived across these languages. The first study consisted of acoustic analyses of sarcastic utterances spoken in English to verify whether particular prosodic cues correspond to English sarcastic speech. Native English speakers produced utterances expressing sarcasm, sincerity, humour, or neutrality. Measures taken from each utterance included fundamental frequency (F0), amplitude, speech rate, harmonics-to-noise ratio (HNR, to probe voice quality), and one-third octave spectral values (to probe resonance). The second study was conducted to ascertain whether specific acoustic features marked sarcasm in Cantonese and how such features compare with English sarcastic prosody. The elicitation and acoustic analysis methods from the first study were applied to similarly-constructed Cantonese utterances spoken by native Cantonese speakers. Direct acoustic comparisons between Cantonese and English sarcasm exemplars were also made. To further test for cross-linguistic prosodic cues of sarcasm and to assess whether sarcasm could be conveyed across languages, a cross-linguistic perceptual study was then performed. A subset of utterances from the first two studies was presented to naive listeners fluent in either Cantonese or English. Listeners had to identify the attitude in each utterance regardless of language of presentation. Sarcastic utterances in English (regardless of text) were marked by lower mean F0 and reductions in HNR and F0 standard deviation (relative to comparison attitudes). Resonance changes, reductions in both speech rate and F0 range signalled sarcasm in conjunction with some vocabulary terms. By contrast, higher mean F0, amplitude range reductions, and F0 range restrictions corresponded with sarcastic utterances spoken in Cantonese regardless of text. For Cantonese, reduced speech rate and higher HNR interacted with certain vocabulary to mark sarcasm. Sarcastic prosody was most distinguished from acoustic features corresponding to sincere utterances in both languages. Direct English-Cantonese comparisons between sarcasm tokens confirmed cross-linguistic differences in sarcastic prosody. Finally, Cantonese and English listeners could identify sarcasm in their native languages but identified sarcastic utterances spoken in the unfamiliar language at chance levels. It was concluded that particular acoustic cues marked sarcastic speech in Cantonese and English, and these patterns of sarcastic prosody were specific to each language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".