Modern TTS systems are capable of creating highly realistic and natural-sounding speech, making them an important tool when designing virtual agents.While sounding highly realistic, the process of customizing such TTS voices remains a complex task, mostly requiring the expertise of specialists within the field.One reason for this is the utilization of deep learning models, which are characterized by their expansive, non-interpretable parameter spaces, restricting the feasibility of manual voice customization.In this paper, we present a novel human-in-the-loop paradigm based on an evolutionary algorithm for directly interacting with the parameter space of a neural TTS model.We integrated our approach into a user-friendly graphical user interface that allows users to efficiently create original voices.Those voices can then be used to equip virtual agents with highly customized TTS capabilities by using an open-source programming interface provided by us.Further, in a first pilot study, we show that VoiceX is an appropriate tool for creating individual, custom voices.

This study investigates whether personalized voice cloning can improve a robot’s likability compared to a design-congruent voice and a distinctly dissimilar voice. Participants interacted with a gender-ambiguous android robot in three different voice conditions. We compared: (1) a personalized voice clone based on the participant’s voice, (2) a design-congruent voice matching the robot’s appearance, and (3) a dissimilar voice, which differs from both the participant’s and the robot’s features.The cloned and design-congruent voices significantly increased likability compared to the dissimilar voice, while anthropomorphism and familiarity showed no significant differences across conditions. Most participants did not immediately recognize their cloned voice until informed that one of the voices was a clone. However, most of the participants were successful when asked to pick out their cloned voice from those used. We assume that voice personalization through similarity to the user improves likability even before the user is aware of this similarity.Our results show that personalized voice cloning is a simple alternative to other methods for the design of robotic voices. It significantly increases robot likability while requiring minimal user effort.

This paper investigates the effects of gender neutralization on the perception of anthropomorphism, gender specificity, and novelty for human voices, comparing spoken and hummed voice modalities. We evaluated gender-neutralized and original voice samples in both spoken and hummed formats using an online survey. Our results confirm that gender-neutralizing filters effectively reduce perceived gender specificity in both modalities, supporting their use in creating gender-neutral voices for humanoid robots. Hummed voices were perceived as more anthropomorphic and less novel than spoken voices, suggesting that non-verbal sound modalities can enhance the human likeness of gender-neutral androids while maintaining gender ambiguity. The study contributes to HRI by highlighting the potential of humming to fulfill users’ expectations of interaction with android robots.

This study examines the impact of gender neutralization on the anthropomorphism of speech signals and is motivated by the need to find a computer-generated voice for our android robot Andrea. A filter was used to gender-neutralize recordings from natural and synthetic voices, which were then pre-tested for gender neutrality. The results of the main experiment showed that gender-neutral voices were less anthropomorphic than gender-specific voices, with the naturalness of the voice having a greater impact than gender neutralization itself. Synthetic voices were rated less anthropomorphic than natural voices, and the additional effect of gender neutralization was stronger for natural voices. Overall, anthropomorphism ranked highest for natural gender-specific voices, followed by natural gender-neutralized voices, synthetic gender-specific, and, finally, synthetic gender-neutralized voices. Gender of the participants had no significant impact. These findings have implications for the development of humanoid and social as well as android robots.