Voicing Uncertainty: How Speech, Text, and Visualizations Influence Decisions with Data Uncertainty
August 15, 2024 Β· Declared Dead Β· π 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks
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Authors
Chase Stokes, Chelsea Sanker, Bridget Cogley, Vidya Setlur
arXiv ID
2408.08438
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks
Last Checked
4 months ago
Abstract
Understanding and communicating data uncertainty is crucial for informed decision-making across various domains, including finance, healthcare, and public policy. This study investigates the impact of gender and acoustic variables on decision-making, confidence, and trust through a crowdsourced experiment. We compared visualization-only representations of uncertainty to text-forward and speech-forward bimodal representations, including multiple synthetic voices across gender. Speech-forward representations led to an increase in risky decisions, and text-forward representations led to lower confidence. Contrary to prior work, speech-forward forecasts did not receive higher ratings of trust. Higher normalized pitch led to a slight increase in decision confidence, but other voice characteristics had minimal impact on decisions and trust. An exploratory analysis of accented speech showed consistent results with the main experiment and additionally indicated lower trust ratings for information presented in Indian and Kenyan accents. The results underscore the importance of considering acoustic and contextual factors in presentation of data uncertainty.
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