Do Accents Speak Louder Than Words? Perceptions of Linguistic Speech Characteristics on Deception Detection
Bibliographic record
Abstract
Using videotaped interviews of beginner, intermediate, and native English speakers, we examined whether observers’ perceptions of linguistic measures of accentedness, temporal fluency, lexicogrammar, and comprehensibility influenced their deception detection. We found that observers could detect differences in speech characteristics between proficiency levels, and that they were less able to detect deception among beginner speakers compared to intermediate and native speakers. Beginner speakers were also afforded more of a truth bias compared to intermediate, but not native speakers. Interestingly, observers’ backgrounds, including prior exposure to non-native speech, did not influence their judgments. Rather, observers’ discrimination and response bias appeared to be most affected by speakers’ fluency and comprehensibility, respectively. This study is one of the first to separate and directly compare perceptions of linguistic characteristics and their role in deception detection. Findings raise questions about equitable deception detection in legal settings.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".