Selecting Emotionally Expressive Neural Voices for Social Robots: Effects on Social Perception in Human-Robot Interaction

Abstract

Vocal expressiveness is a critical determinant of social presence in Human-Robot Interaction, particularly in assistive domains. While Large Language Models have significantly advanced conversational capabilities, standard Textto-Speech engines often lack the emotional depth required for empathic interaction, leading to a perceptual mismatch between the robot's role and its voice. This study investigates the impact of integrating an emotionally adaptive neural TextTo-Speech into the UBTECH Alpha Mini humanoid robot. A two-stage methodology is proposed, comprising a preliminary survey to ensure visual-auditory congruency based on the robot's appearance, followed by a user study comparing a standard baseline against neural vocal variants across different emotional scenarios. Results indicate that neural voices significantly outperform the baseline in terms of perceived warmth, anthropomorphism, and likability. Furthermore, the findings highlight that adapting vocal timbre to the specific emotional context is essential for enhancing the communicative effectiveness and social acceptance of assistive robots.