Robot Voices in Daily Life: Vocal Human-Likeness and Application Context as Determinants of User Acceptance - Schreibelmayr, Simon; Mara, Martina (Frontiers in Psychology, 2022)
The growing popularity of speech interfaces goes hand in hand with the creation of synthetic voices that sound ever more human-like. Previous research has been inconclusive about whether anthropomorphic design features of machines are more likely to be associated with positive user responses or, conversely, with uncanny experiences. To avoid detrimental effects of synthetic voice design, it is therefore crucial to explore which level of human realism is preferred by human interactors and whether their evaluations may vary across different domains of application. In a randomized lab experiment, 165 participants listened to one out of five female-sounding robot voices of varying human realism. We assessed how much participants anthropomorphized the voice (by subjective human-likeness ratings, a name-giving task and an imagination task), how pleasant and eerie they evaluated it, and how much they indicated to accept its application in different domains. Additionally, Big Five personality measures and a tolerance of ambiguity scale were completed. Our results indicate a positive relationship between human-likeness and user acceptance, with the most realistically sounding voice scoring highest in pleasantness and lowest in eeriness. Along with increasing realism, participants were also more likely to assign real human names to the voice (e.g., “Julia” instead of “T380”). In terms of application context, participants overall indicated less acceptance for the use of speech interfaces in social domains (care, companionship) than in others (e.g., information & navigation), though the most human-like voice was rated significantly more acceptable for social application than the remaining four voices. While most personality factors did not prove influential, openness to experience was found to moderate the relationship between voice type and user acceptance in such a way that individuals with higher openness scores rated the most human-like voice even more positively. Study results are discussed in the light of the presented theory and with regard to open research questions in the field of synthetic voice design.