Beyond the monotonic: Enhancing human-robot interaction through affective communication

Abstract

As robots increasingly become part of human environments, their ability to convey empathy and emotional expression is critical for effective interaction. While non-verbal cues, such as facial expressions and body language, have been widely researched, the role of verbal communication - especially affective speech - has received less attention, despite being essential in many human-robot interaction scenarios. This study addresses this gap through a laboratory experiment with 157 participants, investigating how a robot's affective speech influences human perceptions and behavior. To explore the effects of varying intonation and content, we manipulated the robot's speech across three conditions: monotonic-neutral, monotonic-emotional, and expressive-emotional. Key measures included attributions of experience and agency (following the Theory of Mind), perceived trustworthiness (cognitive and affective level), and forgiveness. Additionally, the Balloon Analogue Risk Task (BART) was employed to assess dependence behavior objectively, and a teaching task with intentional robot errors was used to measure behavioral forgiveness. Our findings reveal that emotionally expressive speech enhances the robot's perceived capacity for experience (i.e., the ability to feel emotions) and increases affective trustworthiness. The results further suggest that affective content of speech, rather than intonation, is the decisive factor. Consequently, in future robotic applications, the affective content of a robot's communication may play a more critical role than the emotional tone. However, we did not find significant differences in dependence behavior or forgiveness across the varying levels of affective communication. This suggests that while affective speech can influence emotional perceptions of the robot, it does not necessarily alter behavior.