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

Abstract

Building user trust is critical for the widespread adoption of automated vehicles (AVs) as they become more integrated into our daily lives. 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 age and gender. In an online study involving over 300 U.S. drivers, participants experienced AV explanations delivered in voices that aligned with or differed from their demographic characteristics. The results revealed that users reported significantly higher cognitive and affective trust when the AV voice matched their own. Gender similarity strongly impacted 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 enhance human-AV interaction through more socially attuned and emotionally resonant communication strategies.