TY - GEN A1 - Kühne, Katharina A1 - Fischer, Martin H. A1 - Zhou, Yuefang T1 - The Human Takes It All BT - Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study T2 - Postprints der Universität Potsdam : Humanwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 700 KW - human-robot interaction KW - paralinguistic features KW - synthesized voice KW - text-to-speech KW - uncanny valley Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-491625 SN - 1866-8364 IS - 700 ER - TY - JOUR A1 - Kühne, Katharina A1 - Fischer, Martin H. A1 - Zhou, Yuefang T1 - The Human Takes It All BT - Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study JF - Frontiers in Neurorobotics N2 - 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. KW - human-robot interaction KW - paralinguistic features KW - synthesized voice KW - text-to-speech KW - uncanny valley Y1 - 2020 U6 - https://doi.org/10.3389/fnbot.2020.593732 SN - 1662-5218 VL - 14 PB - Frontiers Research Foundation CY - Lausanne ER -