Effects of Talker Sex and Voice Style of Verbal Cockpit Warnings on Performance
Bibliographic record
Abstract
OBJECTIVE: The effects of talker sex and voice style of verbal cockpit warnings on performance were investigated to help make warning messages distinct from speech on the flight deck. BACKGROUND: Auditory warnings are used in aircraft to alert the crew to hazards and their associated levels of danger. Failing to comply with a warning has led to aviation incidents and accidents. METHODS: Participants were required to monitor the auditory channel and identify the verbal warning while simultaneously performing a visual pursuit tracking task. A male and a female actor annunciated the warning words in three styles: monotone, urgent, and whisper. In Experiment 1, warning words were presented in quiet, and in Experiment 2, they were presented in a background of speech babble that simulated cockpit radio communication. RESULTS: Experiment 1 showed that the monotone and urgent styles resulted in the fastest identification response time (RT) regardless of the talker and listener's sex. Experiment 2 showed that the male talker annunciating in either the monotone or the urgent style resulted in the largest proportion correct and fastest identification RT regardless of the listener's sex. Both experiments showed effects of word semantics on performance. CONCLUSION: Effective use of speech parameters and word semantics can increase the saliency of verbal cockpit warnings. APPLICATION: Potential applications of this research include improving the attention-getting capability of an alerting system, which could lead to increased warning compliance, potentially resulting in fewer incidents and accidents.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".