Voice interaction has entered daily life. Yet, the measurement of voice user experience (voice UX) remains underdeveloped. We took a consensus-based approach to the creation of a new instrument grounded in the work of voice UX researchers. For this, we conducted a systemic review of the literature from ACM, IEEE, and Web of Science ( ) to gather a corpus of instruments, measures, and methodological perspectives. We then carried out two validation studies on a range of computer voices, showing through exploratory ( ) and confirmatory factor analyses ( ) that five underlying factors unify the consensus-derived measures. We offer the 24-item Voice Experience Inventory (VOXI) alongside three comprehensive frameworks on key independent and dependent variables and their relationships, anchored to the material that guided our instrument development process. This work represents a timely and community-centred path towards rigorous and standardized measurement of voice UX.

Communication between people is characterized by a broad range of nonverbal cues. Transferring these cues into the design of robots and other artificial agents that interact with people may foster more natural, inviting, and accessible experiences. In this article, we offer a series of definitive nonverbal codes for human–robot interaction (HRI) that address the five human sensory systems (visual, auditory, haptic, olfactory, and gustatory) drawn from the field of communication studies. We discuss how these codes can be translated into design patterns for HRI using a curated sample of the communication studies and HRI literatures. As nonverbal codes are an essential mode in human communication, we argue that integrating robotic nonverbal codes in HRI will afford robots a feeling of “aliveness” or “social agency” that would otherwise be missing. We end with suggestions for research directions to stimulate work on nonverbal communication within the field of HRI and improve communication between people and robots.

Our research aims at understanding if robots could be good at public speaking, what they need to achieve the level of a good public speaker and how they may surpass a human public speaker. Previous research results indicate that designing a robot speaker by mimicking some of the behaviours of a human speaker is not enough to create an effective robot speech performance. It can in fact be counter productive to strive for human-likeliness. In this paper, we describe how we programmed a toy-like, non-anthropomorphic small robot (the Anki Vector robot) to deliver a speech by extracting pose and facial expression information from the video of a human speaker and loosely retargeting this information to the robot. We also describe our experimental plans to compare Vector’s speech delivery performance with the performance of a more anthropomorphic robot (the SoftBank Pepper robot), which has been programmed to closely mimic the human speaker’s behaviour. They are compared in terms of their ability to evoke the positive affective responses necessary to spark interest, motivate the audience to listen, and engage the audience in meaningful ways.

Social robots acting as stand-ins for speakers or teachers would enable them to reach large audiences from anywhere in the world, increasing the options for distant learning. They would need to be endowed with effective public speaking skills though, in order to deliver their message, entertain, and maintain audience attention.

The goal of this study was to examine user\textquoteright s perception of expressions of empathy by an autonomous system. In a survey eight different components of empathy identified in literature studies and prior tests (Expressing own feelings, Expressing to know what the other feels, Helping, Showing interest, Taking the others perspective, Displaying regard, Situational understanding, and Agreement) were compared to neutral expressions. Differences in participants evaluations were found across the components of empathy as well as individual differences were revealed. Expressions of cognitive empathy (Showing interest, situational understanding) and expressions of empathy of assistance (helping) were perceived positively by participants. However, expressions of affective empathy (expressing own feelings, expressing to know what the other feels) received mainly negative ratings. Cluster analysis revealed individual differences especially for items relating to affective empathy. Whereas one group of participants identified in the cluster analysis rated expressions of affective empathy negatively, a second group of participants rated these expressions positively. Furthermore, large differences across participants also existed for taking the other's perspective, a component of cognitive empathy. Integrating expressions of empathy in human-machine interaction is a sensitive issue and designers must carefully choose what components of empathy are adequate depending on the situational circumstances and the targeted user group.