This paper explores how feminist and queer theories can inform voice design in technology, particularly in Human-Computer Interaction (HCI). It argues that biological sex and gender are socially constructed and performative, and that voice is a site where identity is both enacted and interpreted. Building on this framework, the paper examines the political and cultural implications of labels such as “ambiguous”, “queer” and “nonbinary” in voice design. While “ambiguous” voices aim to reduce gendering broadly, “queer” and “nonbinary” voices intentionally represent gender-non-conforming people and challenge binary thinking. To ground this analysis in community perspectives, we report findings from a survey with nonbinary participants, examining how they label voices constructed from gender-expansive individuals and which terms they find most affirming. With this work, we offer practical guidelines for labelling voices in ways that affirm queer and nonbinary identities, clarifying when terms like “queer” and “nonbinary” are preferable and when “ambiguous” may be appropriate. Recognising these distinctions is key to ethical, inclusive design.

The “gender” of intelligent agents, virtual characters, social robots, and other agentic machines has emerged as a fundamental topic in studies of people’s interactions with computers. Perceptions of agent gender can help explain user attitudes and behaviours—from preferences to toxicity to stereotyping—across a variety of systems and contexts of use. Yet, standards in capturing perceptions of agent gender do not exist. A scoping review was conducted to clarify how agent gender has been operationalized—labelled, defined, and measured—as a perceptual variable. One-third of studies manipulated but did not measure agent gender. Norms in operationalizations remain obscure, limiting comprehension of results, congruity in measurement, and comparability for meta-analyses. The dominance of the gender binary model and latent anthropocentrism have placed arbitrary limits on knowledge generation and reified the status quo. We contribute a systematically-developed and theory-driven meta-level framework that offers operational clarity and practical guidance for greater rigour and inclusivity.

Robot design often involves gendered choices that shape Human–Robot Interaction. Voice is a key channel through which gendering occurs, yet little is known about how it influences people’s mental images of robots. This study examines how ambiguous, feminine, and masculine voices affect physical robot design. Participants (N = 45) listened to robot voices (ambiguous, feminine, masculine), built a physical prototype, took part in an interview to explain their design process, and concluded by evaluating both the voice and the robot prototype they built. The findings show that although participants’ explicit ratings of the robots showed no differences across conditions, analyses of the physical prototypes and interview data revealed consistent patterns, suggesting that voice strongly shaped design choices. Specifically, we found that ambiguous voices led to less human-like forms and more hybrid human-machine-like and masculine forms, whereas masculine voices encouraged more human-like prototypes. The results suggest that starting robot design from voice, particularly ambiguous voices, helps reduce gendering and fosters more inclusive robots.

Human–Robot Interaction (HRI) research is starting to engage with sustainability, yet the field remains tied to economic models that assume continual growth, rapid technological development, and market expansion. This economic growth orientation raises questions about whether HRI can genuinely support ecological responsibility, given the resource intensity of robotics research, production, and deployment. In this contribution, we introduce a post-growth perspective to reframe the relationship between robotics, sustainability, and society. We argue that rather than striving for `green growth' within existing economic structures, HRI should engage critically with concepts such as degrowth and post-capitalism. By shifting attention from growth to development, we invite the community to consider what robotic futures are worth pursuing and for whom.

Voice interfaces come in many forms in Human-Computer Interaction (HCI), such as voice assistants and robots. These are often gendered, i.e. they sound masculine or feminine. Recently, there has been a surge in creating gender-ambiguous voices, aiming to make voice interfaces more inclusive and less prone to stereotyping. In this paper, we present the first systematic review of research on gender-ambiguous voices in HCI literature, with an in-depth analysis of 36 articles. We report on the definition and availability of gender-ambiguous voices, creation methods, user perception and evaluation techniques. We conclude with several concrete action points: clarifying key terminology and definitions for terms such as gender-ambiguous, gender-neutral, and non-binary; conducting an initial acoustic analysis of gender-ambiguous voices; taking initial steps toward standardising evaluation metrics for these voices; establishing an open-source repository of gender-ambiguous voices; and developing a framework for their creation and use. These recommendations provide important insights for fostering the development and adoption of inclusive voice technologies.

“Sustainability −4- HRI, HRI −4-Sustainability” offers hands-on engagement with the HRI 2025 conference theme, “Robots for a Sustainable World”. This workshop will explore the relationship between HRI and sustainable development and stimulate discussion on how we can make our own research practices more sustainable. We propose a full-day workshop, featuring morning discussions with sustainability experts and activists, and an afternoon hands-on activity aimed at understanding how robotics research can help in creating sustainable futures. We will broadly answer the following questions: How can we, as robotic researchers, help address sustainable development responsibly? Equally, how can we ensure that our HRI research practices minimises ecological footprints and operates within ethical and sustainable frameworks? We envision two practical outcomes of this workshop: a “sustainability statement” that can be submitted together with future HRI research papers, and a paper gathering insights and reflections from the workshop. We welcome researchers and students at any career stage and from any subfield of HRI to attend and contribute.

When a robot is developed, it usually has well-defined physical and occupational characteristics that are often gendered. This can influence the Human-Robot Interaction (HRI) experience and also risks fostering harmful gender stereotypes in society. One factor contributing to gender attribution to robots is their voice, which can be manipulated and changed to fit any need. Thanks to the recent development of gender-ambiguous voices, this provides an interesting research space to investigate if this modality can reduce robot gendering. Our research investigates whether these voices influence how people picture a robot in different occupational contexts. We asked participants to sketch a robot after listening to either a gender-ambiguous voice presenting a neutral, feminine, masculine scenario, a female voice presenting a feminine scenario, or a male voice presenting a masculine scenario. Results indicate that an ambiguous voice influences gender associations, as participants were less likely to assign a gender to their sketched robots when exposed to an ambiguous voice, compared to a male or female voice. This finding highlights the potential of deploying ambiguous voices to reduce gender biases in HRI, even in stereotypically gendered roles, such as security guards or secretaries.

The use of drones is expected to increase for delivering groceries or medical equipment to individuals. Understanding how people perceive drone behavior, specifically in terms of approach trajectories and delivery methods, and identifying factors that induce feelings of uncertainty is crucial for perceived safety and trust. This virtual reality experiment investigated the impact of drone approach trajectories and delivery methods on feelings of uncertainty. Forty-five participants observed a drone approaching in an orthogonal or a curved path and either, delivering packages by landing or using a cable while hovering above eye level. We found that participants felt uncertain and unsafe, especially when looking up at drones approaching with orthogonal paths. Curved paths led to lower feelings of uncertainty, with comments such as being more natural, trustful, and safe. Feelings of uncertainty arose while landing on the ground due to altitude changes and potential collision concerns. Using a cable instead of actually landing for delivery reduced feelings of uncertainty and increased trust. The study recommends drones avoid hovering near humans, especially after landing. Furthermore, the study suggests exploring design solutions, including design aesthetics and human–machine interfaces, that clearly convey drone intentions to help reduce feelings of uncertainty.

Trust is essential for social interactions, including those between humans and social artificial agents, such as robots. Several factors and combinations thereof can contribute to the formation of trust and, importantly in the case of machines that work with a certain margin of error, to its maintenance and repair after it has been breached. In this article, we present the results of a study aimed at investigating the role of robot voice and chosen repair strategy on trust formation and repair in a collaborative task. People helped a robot navigate through a maze, and the robot made mistakes at pre-defined points during the navigation. Via in-game behaviour and follow-up questionnaires, we could measure people’s trust towards the robot. We found that people trusted the robot speaking with a state-of-the-art synthetic voice more than with the default robot voice in the game, even though they indicated the opposite in the questionnaires. Additionally, we found that three repair strategies that people use in human-human interaction (justification of the mistake, promise to be better and denial of the mistake) work also in human-robot interaction.

Multisensory integration influences emotional perception, as the McGurk effect demonstrates for the communication between humans. Human physiology implicitly links the production of visual features with other modes like the audio channel: Face muscles responsible for a smiling face also stretch the vocal cords that result in a characteristic smiling voice. For artificial agents capable of multimodal expression, this linkage is modeled explicitly. In our studies, we observe the influence of visual and audio channels on the perception of the agents' emotional expression. We created videos of virtual characters and social robots either with matching or mismatching emotional expressions in the audio and visual channels. In two online studies, we measured the agents' perceived valence and arousal. Our results consistently lend support to the ‘emotional McGurk effect' hypothesis, according to which face transmits valence information, and voice transmits arousal. When dealing with dynamic virtual characters, visual information is enough to convey both valence and arousal, and thus audio expressivity need not be congruent. When dealing with robots with fixed facial expressions, however, both visual and audio information need to be present to convey the intended expression.

The next generation of robots is expected to work collaboratively with humans in natural (dynamic) settings. For this, it is important to properly study and model human factors, so that the AI and Robotic models can include them to enable robust Human-Robot Collaborations. This will enable safe and trustworthy hybrid decision-making approaches – Responsible AI – thereby streamlining robust collaborations (as per human-centred expectations). This interdisciplinary workshop will focus on the intersection of Cognitive Human Factors, Interpretable & Explainable AI methods, Social Interaction, and Human-Centred Robotics to stimulate novel long-range avenues for innovative human-centred collaborative methods in real-world contexts.

Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatio-temporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and preferences in the Real-Time Rapidly Exploring Random Tree (RT-RRT*) motion planning algorithm in an environment with dynamic obstacles. We propose a cost function that guides the algorithm towards the asymptotically most robust solution, i.e. a plan that maximally adheres to the STL specification. In experiments, we applied our method to a social navigation case, where the STL specification captures spatio-temporal preferences on how a mobile robot should avoid an incoming human in a shared space. Our results show that our approach leads to plans adhering to the STL specification, while ensuring efficient cost computation.

Trust is essential for social interactions, including those between humans and social artificial agents, such as robots. Several robot-related factors can contribute to the formation of trust. However, previous work has often treated trust as an absolute concept, whereas it is highly context-dependent, and it is possible that some robot-related features will influence trust in some contexts, but not in others. In this paper, we present the results of two video-based online studies aimed at investigating the role of robot voice and empathic behaviour on trust formation in a general context as well as in a task-specific context. We found that voice influences trust in the specific context, with no effect of voice or empathic behaviour in the general context. Thus, context mediated whether robot-related features play a role in people’s trust formation towards robots.

When deploying robots, its physical characteristics, role, and tasks are often fixed. Such factors can also be associated with gender stereotypes among humans, which then transfer to the robots. One factor that can induce gendering but is comparatively easy to change is the robot’s voice. Designing voice in a way that interferes with fixed factors might therefore be a way to reduce gender stereotypes in human-robot interaction contexts. To this end, we have conducted a video-based online study to investigate how factors that might inspire gendering of a robot interact. In particular, we investigated how giving the robot a gender-ambiguous voice can affect perception of the robot. We compared assessments (n=111) of videos in which a robot’s body presentation and occupation mis/matched with human gender stereotypes. We found evidence that a gender-ambiguous voice can reduce gendering of a robot endowed with stereotypically feminine or masculine attributes. The results can inform more just robot design while opening new questions regarding the phenomenon of robot gendering.

Sound can benefit human-robot interaction, but little work has explored questions on the design of nonverbal sound for robots. The unique confluence of sound design and robotics expertise complicates these questions, as most roboticists do not have sound design expertise, necessitating collaborations with sound designers. We sought to understand how roboticists and sound designers approach the problem of robot sound design through two qualitative studies. The first study followed discussions by robotics researchers in focus groups, where these experts described motivations to add robot sound for various purposes. The second study guided music technology students through a generative activity for robot sound design; these sound designers in-training demonstrated high variability in design intent, processes, and inspiration. To unify the two perspectives, we structured recommendations through the design thinking framework, a popular design process. The insights provided in this work may aid roboticists in implementing helpful sounds in their robots, encourage sound designers to enter into collaborations on robot sound, and give key tips and warnings to both.

Non-verbal communication is important in HRI, particularly when humans and robots do not need to actively engage in a task together, but rather they co-exist in a shared space. Robots might still need to communicate states such as urgency or availability, and where they intend to go, to avoid collisions and disruptions. Sounds could be used to communicate such states and intentions in an intuitive and non-disruptive way. Here, we propose a multi-layer classification system for displaying various robot information simultaneously via sound. We first conceptualise which robot features could be displayed (robot size, speed, availability for interaction, urgency, and directionality); we then map them to a set of audio parameters. The designed sounds were then evaluated in five online studies, where people listened to the sounds and were asked to identify the associated robot features. The sounds were generally understood as intended by participants, especially when they were evaluated one feature at a time, and partially when they were evaluated two features simultaneously. The results of these evaluations suggest that sounds can be successfully used to communicate robot states and intended actions implicitly and intuitively.

Recent work identified a concerning trend of disproportional gender representation in research participants in Human–Computer Interaction (HCI). Motivated by the fact that Human–Robot Interaction (HRI) shares many participant practices with HCI, we explored whether this trend is mirrored in our field. By producing a dataset covering participant gender representation in all 684 full papers published at the HRI conference from 2006–2021, we identify current trends in HRI research participation. We find an over-representation of men in research participants to date, as well as inconsistent and/or incomplete gender reporting, which typically engages in a binary treatment of gender at odds with published best practice guidelines. We further examine if and how participant gender has been considered in user studies to date, in-line with current discourse surrounding the importance and/or potential risks of gender based analyses. Finally, we complement this with a survey of HRI researchers to examine correlations between who is doing with the who is taking part, to further reflect on factors which seemingly influence gender bias in research participation across different sub-fields of HRI. Through our analysis, we identify areas for improvement, but also reason for optimism, and derive some practical suggestions for HRI researchers going forward.

When a robot aims to comprehend its human partner's request by identifying the referenced objects in Human-Robot Conversation, ambiguities can occur because the environment might contain many similar objects or the objects described in the request might be unknown to the robot. In the case of ambiguities, most of the systems ask users to repeat their request, which assumes that the robot is familiar with all of the objects in the environment. This assumption might lead to task failure, especially in complex real-world environments. In this paper, we address this challenge by presenting an interactive system that asks for follow-up clarifications to disambiguate the described objects using the pieces of information that the robot could understand from the request and the objects in the environment that are known to the robot. To evaluate our system while disambiguating the referenced objects, we conducted a user study with 63 participants. We analyzed the interactions when the robot asked for clarifications and when it asked users to redescribe the same object. Our results show that generating followup clarification questions helped the robot correctly identify the described objects with fewer attempts (i.e., conversational turns). Also, when people were asked clarification questions, they perceived the task as easier, and they evaluated the task understanding and competence of the robot as higher. Our code and anonymized dataset are publicly available11https://github.com/IrmakDogan/Resolving-Ambiguities.

Following the success of the first edition of Robo-Identity, the second edition will provide an opportunity to expand the discussion about artificial identity. This year, we are focusing on emotions that are expressed through speech and voice. Synthetic voices of robots can resemble and are becoming indistinguishable from expressive human voices. This can be an opportunity and a constraint in expressing emotional speech that can (falsely) convey a human-like identity that can mislead people, leading to ethical issues. How should we envision an agent's artificial identity? In what ways should we have robots that maintain a machine-like stance, e.g., through robotic speech, and should emotional expressions that are increasingly human-like be seen as design opportunities? These are not mutually exclusive concerns. As this discussion needs to be conducted in a multidisciplinary manner, we welcome perspectives on challenges and opportunities from variety of fields. For this year's edition, the special theme will be “speech, emotion and artificial identity”.

This paper is interested in formalizing human trajectories in human-robot encounters. Inspired by robot navigation tasks in human-crowded environments, we consider the case where a human and a robot walk towards each other, and where humans have to avoid colliding with the incoming robot. Further, humans may describe different be-haviors, ranging from being in a hurry/minimizing completion time to maximizing safety. We propose a decision tree-based algorithm to extract STL formulae from multi-label data. Our inference algorithm learns STL specifications from data containing multiple classes, where instances can be labelled by one or many classes. We base our evaluation on a dataset of trajectories collected through an online study reproducing human-robot encounters.

Emotional expressivity is essential for successful Human-Robot Interaction. However, robots often have different levels of expressivity in their face and voice. Here we ask whether this modality mismatch influences human behaviour and perception of the robot. Participants played a cooperative task with a robot that displayed matched and mismatched smiling expressions in the face and voice. Emotional expressivity did not influence acceptance of robot’s recommendations or subjective evaluations of the robot. However, we found that the robot had overall a higher social influence than a virtual character, and was evaluated more positively.

Multisensory integration influences emotional perception, as the McGurk effect demonstrates for the communication between humans. Human physiology implicitly links the production of visual features with other modes like the audio channel: Face muscles responsible for a smiling face also stretch the vocal cords that results in a characteristic smiling voice. For artificial agents capable of multimodal expression, this linkage is modeled explicitly. In our study, we observe the influence of visual and audio channel on the perception of the agent’s emotional state. We created two virtual characters to control for anthropomorphic appearance. We record videos of these agents either with matching or mismatching emotional expression in the audio and visual channel. In an online study we measured the agent’s perceived valence and arousal. Our results show that a matched smiling voice and smiling face increase both dimensions of the Circumplex model of emotions: ratings of valence and arousal grow. When the channels present conflicting information, any type of smiling results in higher arousal rating, but only the visual channel increases the perceived valence. When engineers are constrained in their design choices, we suggest they should give precedence to convey the artificial agent’s emotional state through the visual channel.

Artificial agents’ smiling behaviour is likely to influence their likeability and the quality of user experience. While studies of human interaction highlight the importance of smile dynamics, this feature is often lacking in artificial agents, presenting a design opportunity. We developed a virtual motivational therapist with four smiling behaviours, varying in terms of quality and dynamism . We video-recorded experimental sessions with participants who posed as patients in a therapy session. The data were analysed combining a mix of quantitative and qualitative methods, focusing on participants’ own facial expressions during the interaction. Results suggest that the condition driven using data from a real therapist, where smiles are dynamic and occur at specific moments, is the most effective. We further discuss the particular importance of smile as a multipurpose emotional display in human-machine interaction.

Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. Whilst this issue has been proposed as an essential subject to be included in the new AI curricula for schools, research has shown that it is a difficult topic to grasp by students. We propose an educational platform tailored to raise the awareness of gender bias in supervised learning, with the novelty of using Grad-CAM as an explainability technique that enables the classifier to visually explain its own predictions. Our study demonstrates that preadolescents (N=78, age 10-14) significantly improve their understanding of the concept of bias in terms of gender discrimination, increasing their ability to recognize biased predictions when they interact with the interpretable model, highlighting its suitability for educational programs.

Two people walking towards each other in a colliding course is an everyday problem of human-human interaction. In spite of the different environmental and individual factors that might jeopardise successful human trajectories, people are generally skilled at avoiding crashing into each other. However, it is not clear if the same strategies will apply when a human is in a colliding course with a robot, nor which (if any) robot-related factors will influence the human's decision to swerve or not. In this work, we present the results of an online study where participants walked towards a virtual robot that differed in terms of anthropomorphism and perceived autonomy, and had to decide whether to swerve, or continue straight. The experiment was inspired by the game-theoretic game of chicken. We found that people performed more swerving actions when they believed the robot to be teleoperated by another participant. When they swerved, they also swerved closer to the robot with high levels of human-likeness, and farther away from the robot with low anthropomorphism score, suggesting a higher uncertainty about the mechanical-looking robot's intentions. These results are discussed in the context of socially-aware robot navigation, and will be used to design novel algorithms for robot trajectories that take robot-related differences into account.

Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style.

In this paper, we are interested in formalizing human trajectories in human-robot encounters. We consider a particular case where a human and a robot walk towards each other. A question that arises is whether, when, and how humans will deviate from their trajectory to avoid a collision. These human trajectories can then be used to generate socially acceptable robot trajectories. To model these trajectories, we propose a data-driven algorithm to extract a formal specification expressed in Signal Temporal Logic with probabilistic predicates. We evaluated our method on trajectories collected through an online study where participants had to avoid colliding with a robot in a shared environment. Further, we demonstrate that probabilistic STL is a suitable formalism to depict human behavior, choices and preferences in specific scenarios of social navigation.

Emotional expressivity is essential for human interactions, informing both perception and decision-making. Here, we examine whether creating an audio-visual emotional channel mismatch influences decision-making in a cooperative task with a virtual character. We created a virtual character that was either congruent in its emotional expression (smiling in the face and voice) or incongruent (smiling in only one channel). People (N = 98) evaluated the character in terms of valence and arousal in an online study; then, visitors in a museum played the “lunar survival task” with the character over three experiments (N = 597, 78, 101, respectively). Exploratory results suggest that multi-modal expressions are perceived, and reacted upon, differently than unimodal expressions, supporting previous theories of audio-visual integration.

Conducting user studies is a crucial component in many scientific fields. While some studies require participants to be physically present, other studies can be conducted both physically (e.g. in-lab) and online (e.g. via crowdsourcing). Inviting participants to the lab can be a time-consuming and logistically difficult endeavor, not to mention that sometimes research groups might not be able to run in-lab experiments, because of, for example, a pandemic. Crowdsourcing platforms such as Amazon Mechanical Turk (AMT) or Prolific can therefore be a suitable alternative to run certain experiments, such as evaluating virtual agents. Although previous studies investigated the use of crowdsourcing platforms for running experiments, there is still uncertainty as to whether the results are reliable for perceptual studies. Here we replicate a previous experiment where participants evaluated a gesture generation model for virtual agents. The experiment is conducted across three participant pools - in-lab, Prolific, and AMT - having similar demographics across the in-lab participants and the Prolific platform. Our results show no difference between the three participant pools in regards to their evaluations of the gesture generation models and their reliability scores. The results indicate that online platforms can successfully be used for perceptual evaluations of this kind.

Social robots have a recognizable physical appearance, a distinct voice, and interact with users in specific contexts. Previous research has suggested a `matching hypothesis', which seeks to rationalise how people judge a robot's appropriateness for a task by its appearance. Other research has extended this to cover combinations of robot voices and appearances. In this paper, we examine the missing connection between robot voice, robot appearance, and deployment context. In so doing, we asked participants to match a robot image to a voice within a defined interaction context. We selected widely available social robots, identified task contexts they are used in, and manipulated the voices in terms of gender, naturalness, and accent. We found that the task context mediates the `matching hypothesis'. People consistently selected a robot based on a vocal feature for a certain context, and a different robot based on the same vocal feature for another context. We suggest that robot voice design should take advantage of current technology that enables the creation and tuning of custom voices. They are a flexible tool to increase perception of appropriateness, which has a positive influence on Human-Robot Interaction.

Accents are vocal features that immediately tell a listener whether a speaker comes from their same place, i.e. whether they share a social group. This in-groupness is important, as people tend to prefer interacting with others who belong to their same groups. Accents also evoke attitudinal responses based on their supposed prestigious status. These accent-based perceptions might affect interactions between humans and robots. Yet, very few studies so far have investigated the effect of accented robot speakers on users' perceptions and behaviour, and none have collected users' explicit preferences on robot accents. In this paper we present results from a survey of over 500 British speakers, who indicated what accent they would like a robot to have. The biggest proportion of participants wanted a robot to have a Standard Southern British English (SSBE) accent, followed by an Irish accent. Crucially, very few people wanted a robot with their same accent, or with a machine-like voice. These explicit preferences might not turn out to predict more successful interactions, also because of the unrealistic expectations that such human-like vocal features might generate in a user. Nonetheless, it seems that people have an idea of how their artificial companions should sound like, and this preference should be considered when designing them.

It is well established that a robot's visual appearance plays a significant role in how it is perceived. Considerable time and resources are usually dedicated to help ensure that the visual aesthetics of social robots are pleasing to users and helps facilitate clear communication. However, relatively little consideration is given to how the voice of the robot should sound, which may have adverse effects on acceptance and clarity of communication. In this study, we explore the mental images people form when they hear robots speaking. In our experiment, participants listened to several voices, and for each voice they were asked to choose a robot, from a selection of eight commonly used social robot platforms, that was best suited to have that voice. The voices were manipulated in terms of naturalness, gender, and accent. Results showed that a) participants seldom matched robots with the voices that were used in previous HRI studies, b) the gender and naturalness vocal manipulations strongly affected participants' selection, and c) the linguistic content of the utterances spoken by the voices does not affect people's selection. This finding suggests that people associate voices with robot pictures, even when the content of spoken utterances was unintelligible. Our findings indicate that both a robot's voice and its appearance contribute to robot perception. Thus, giving a mismatched voice to a robot might introduce a confounding effect in HRI studies. We therefore suggest that voice design should be considered more thoroughly when planning spoken human-robot interactions.

Emotional expressivity can boost trust in human-human and human-machine interaction. As a multimodal phenomenon, previous research argued that a mismatch in the expressive channels provides evidence of joint audio-video emotional processing. However, while previous work studied this from the point of view of emotion recognition and processing, not much is known about what effect a multimodal agent would have on a human-agent interaction task. Also, agent appearance could influence this interaction too. Here we manipulated the agent’s multimodal emotional expression (”smiling face” and ”smiling voice”, or both) and agent type (photorealistic or cartoon-like virtual human) and assessed people’s trust toward this agent. We measured trust using a mixed-methods approach, combining behavioural data from a survival task, questionnaire ratings and qualitative comments. These methods gave different results: while people commented on the importance of emotional expressivity in the agent’s voice, this factor had limited influence on trusting behaviours; while people rated the cartoon-like agent on several traits higher than the photorealistic one, the agent’s style also was not the most influential feature on people’s trusting behaviour. These results highlight the contribution of a mixed-methods approach in human-machine interaction, as both explicit and implicit perception and behaviour will contribute to the success of the interaction.

Correctly interpreting an interlocutor's emotional expression is paramount to a successful interaction. But what happens when one of the interlocutors is a machine? The facilitation of human-machine communication and cooperation is of growing importance as smartphones, autonomous cars, or social robots increasingly pervade human social spaces. Previous research has shown that emotionally expressive virtual characters generally elicit higher cooperation and trust than 'neutral' ones. Since emotional expressions are multi-modal, and given that virtual characters can be designed to our liking in all their components, would a mismatch in the emotion expressed in the face and voice influence people's cooperation with a virtual character? We developed a game where people had to cooperate with a virtual character in order to survive on the moon. The character's face and voice were designed to either smile or not, resulting in 4 conditions: smiling voice and face, neutral voice and face, smiling voice only (neutral face), smiling face only (neutral voice). The experiment was set up in a museum over the course of several weeks; we report preliminary results from over 500 visitors, showing that people tend to trust the virtual character in the mismatched condition with the smiling face and neutral voice more. This might be because the two channels express different aspects of an emotion, as previously suggested.