Trust and Accent: How Speaker Accent Influences Interaction with Humanoid Robots

In the field of human–robot interaction (HRI), researchers have extensively examined the role of social robot characteristics and how these can influence human–robot relationships. In particular, the robot’s voice is one of the most studied aspects, with numerous studies focusing on specific features such as tone, frequency, pitch, and gender. The robot’s voice represents a powerful social signal, whose design can influence people’s affective evaluations and acceptance of robots. With regard to language, however, relatively few studies have investigated the role of a robot’s accent (native or foreign). This experimental study therefore explores the influence of native accent on trust in robots. The study was conducted on two different samples: 60 Italian participants and 37 Arabic participants. Participants listened to two robot presentations in their native language: one delivered with a native accent and the other with a foreign accent. After listening to both presentations, participants were asked to indicate which robot they trusted. The results showed a 77.3% preference for the robot speaking with a native accent, compared to 22.7% for the robot with foreign accent. These findings demonstrate that, regardless of the language (Italian or Arabic), accent significantly influences the choice to invest trust in the robot, supporting the similarity-attraction effect. Accent calibration thus emerges as a low-cost, high-impact parameter in socially assistive and commercial robotics. Since accent influences trust-based delegation, voice design should be strategically adapted in service, healthcare, education, and customer-facing contexts.