AUTHOR=Kühne Katharina , Fischer Martin H. , Zhou Yuefang 

TITLE=The Human Takes It All: Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2020.593732

DOI=10.3389/fnbot.2020.593732

ISSN=1662-5218

ABSTRACT=Background: The increasing involvement of social robots in human lives raises the question how humans perceive social robots. Little is known about human perception of synthesized voices. 
Aim: To investigate which synthesized voice parameters predict the speaker’s eeriness and likability; to determine if individual listener characteristics (e.g., personality, attitude towards robots, age) influence synthesized voice evaluations; and to explore which paralinguistic features subjectively distinguish humans from robots/artificial agents.
Methods: 95 adults (62 females) listened to randomly presented three categories of audio-clips: synthesized (IBM Watson), humanoid (robot Sophia, Hanson Robotics) and human voices (5 clips/category). Voices were rated on intelligibility, prosody, trustworthiness, confidence, enthusiasm, pleasantness, human-likeness and naturalness. Speakers were rated on appeal, credibility, human-likeness, likability and eeriness. Participants’ personality traits, attitudes to robots and demographics were obtained. 
Results: The human voice and human speaker characteristics received reliably higher scores on all dimensions except for eeriness. Synthesized voice ratings were positively related to participants’ agreeableness and neuroticism. Females rated synthesized voices more positively on most dimensions. Surprisingly, interest in social robots and attitudes to robots played almost no role in voice evaluation. Contrary to the expectations of an uncanny valley, the higher the ratings of the human-likeness for both the voice and the speaker characteristics were, the less eerie they seemed to the participants. Moreover, the more human-like the speaker voice was the more it was liked by the participants. The latter was only applicable to one of the synthesized voices. Finally, pleasantness and trustworthiness of the synthesized voice predicted the likability of the speaker. Qualitative content analysis identified intonation, sound, emotion and imageability/embodiment as diagnostic features. 
Discussion: Humans clearly prefer human voices, but manipulating diagnostic speech features might increase acceptance of synthesized voices and thereby support human-robot interaction. No evidence for an auditory uncanny valley was found.