In an experiment we manipulated a robot's voice in two ways: First, we varied robot gender; second, we equipped the robot with a human-like or a robot-like synthesized voice. Moreover, we took into account user gender and tested effects of these factors on human-robot acceptance, psychological closeness and psychological anthropomorphism. When participants formed an impression of a same-gender robot, the robot was perceived more positively. Participants also felt more psychological closeness to the same-gender robot. Similarly, the same-gender robot was anthropomorphized more strongly, but only when it utilized a human-like voice. Results indicate that a projection mechanism could underlie these effects.

A recent theoretical framework on anthropomorphism emphasizes the role of elicited agent knowledge in anthropomorphic inferences about nonhuman entities. According to the Three-Factor Model of psychological Anthropomorphism, people use anthropocentric knowledge structures when judging unfamiliar objects (e.g., robots). In the present research, our goal was to manipulate the accessibility of such elicited agent knowledge by varying features of a robot's voice: Specifically, we examined effects of vocal cues that reflected both gender of robot (i.e., a male vs. female voice) and voice type (i.e., a human-like vs. robot-like voice). This was done to test the impact of these vocal features on anthropomorphic inferences about the robot and on human-robot interaction (HRI) acceptance. Our results demonstrate that a robot's vocal cues clearly influence subsequent judgments of the robot and particularly so, when participant gender taken into account. Implications of our research for robotics will be discussed.

This paper presents the first application of polynomial modeling as a means for validating phonological pitch accent labels to German data. It is compared to traditional phonetic analysis (measuring minima, maxima, alignment). The traditional method fares better in classification, but results are comparable in statistical accent pair testing. Robustness tests show that pitch correction is necessary in both cases. The approaches are discussed in terms of their practicability, applicability to other domains of research and interpretability of their results.