2024/08/15 by Chase Stokes, Chelsea Sanker, Stokes, Chase +5 · 1 citation
Computer Science · #Advanced Text Analysis Techniques #FOS: Computer and information sciences #H.5.0 #Human-Computer Interaction (cs.HC) #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.2408.08438
openalex publication_date 2024/08/15 · openalex created_date 2024/09/13 · openalex updated_date 2026/07/28
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.