Presentation

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Voice Similarity and Its Impact on Cognitive and Affective Trust in Automated Vehicles

DescriptionAs automated vehicles (AVs) become more integrated into our daily lives, building user trust is critical to their widespread adoption. This study explores how the voice used by AVs can influence two types of trust: cognitive trust (belief in the AV’s competence and reliability) and affective trust (emotional connection with the AV). Drawing from similarity-attraction theory, the research investigates whether users are more likely to trust AVs whose voices match their own age and gender. In an online study with over 300 U.S. drivers, participants experienced AV explanations delivered in voices that either aligned with or differed from their demographic characteristics. Results revealed that users reported significantly higher trust, both cognitive and affective, when the AV voice matched their own. Gender similarity had a strong impact on both types of trust, while age similarity mainly affected affective trust. These findings highlight the power of personalized voice design in making AVs feel more relatable and trustworthy. This research offers valuable insights for designers and developers aiming to improve human-AV interaction through more socially attuned and emotionally resonant communication strategies.

Contributors

Qiaoning Zhang

Arizona State University

X. Jessie Yang

University of Michigan