BarryWhaptics: Towards Countering Social Biases Using Real-Time Haptic Enhancement of Voice
Bibliographic record
Abstract
Studies suggest that imbalances in speaking opportunities during meetings often lead to sub-optimal meeting outcomes. These imbalances can be due to a variety of reasons, including people's perception of speakers and their voice. Indeed, speakers with higher pitched voices were shown to be perceived as having lower leadership ability. In an attempt at countering such voice-pitch related biases, this work introduces BarryWhaptics, a real-time speech-to-haptics conversion system that leverages multimodal perception to alter the listener's perception of a speaker. The system operates by augmenting human speech with vibration, applying more intense vibrations to voices that would ordinarily be considered low in dominance. Results from a pilot study assessing the influence of the system in a decision-making task demonstrate that it can meaningfully influence how users choose to follow instructions given by one speaker over another.
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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) | 0.078 | 0.003 |
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; both teacher heads agree on what is shown here.
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".