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

Abstract

Background: The increasing involvement of social robots in human lives raises the question as to 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 voice likability; to determine if individual listener characteristics (e.g., personality, attitude toward 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 audio-clips of three categories: synthesized (Watson, IBM), humanoid (robot Sophia, Hanson Robotics), and human voices (five clips/category). Voices were rated on intelligibility, prosody, trustworthiness, confidence, enthusiasm, pleasantness, human-likeness, likability, and naturalness. Speakers were rated on appeal, credibility, human-likeness, 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 toward robots played almost no role in voice evaluation. Contrary to the expectations of an uncanny valley, when the ratings of human-likeness for both the voice and the speaker characteristics were higher, they seemed less eerie to the participants. Moreover, when the speaker's voice was more humanlike, it was more liked by the participants. This latter point was only applicable to one of the synthesized voices. Finally, pleasantness and trustworthiness of the synthesized voice predicted the likability of the speaker's voice. 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. There is limited evidence that human-likeness of a voice is negatively linked to the perceived eeriness of the speaker.

A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

“…Voice distortion: Distorting natural human voices as auditory stimuli [e.g., Baird et al 2018;Kühne et al 2020]. This technique has been used to test whether the UV effect can occur solely within audition.…”

Robot Voices in Daily Life: Vocal Human-Likeness and Application Context as Determinants of User Acceptance

“…To date, we know only little about whether realistically human-sounding computer voices would elicit particularly positive or negative user responses, and if it matters whether we think of a more social application such as a talking care robot or a more formal one such as a financial assistant. In a recent attempt to shed light on this matter, Kühne et al (2020) found, contrary to their expectations, that participants generally liked highly human-like computer voices more than synthetically sounding ones. Against the background of the popular Uncanny Valley hypothesis (Mori, 1970) and empirical findings on visual or behavioral human-likeness in robots (Bartneck et al, 2007;Mara and Appel, 2015a,b;Appel et al, 2016;Mathur and Reichling, 2016), however, it could be assumed that a too realistic imitation of the human would lead to aversive responses.…”

Public Perceptions Towards Synthetic Voice Technology

“…When ordering at drive thru window or calling a travel agency, respondents indicated that the use of a human-like voice could set unrealistic expectations about the types of capabilities and interactions available. Other work on perceptions of synthetic voices has noted similar issues (Kühne, Fischer, & Zhou, 2020;Poushneh, 2021). To help address the potential gap between user expectations and the functional capabilities of a highfidelity computer-generated voice, some respondents emphasized the importance of a disclaimer.…”