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A growing literature on speech interruptions describes how people interrupt one another with speech, but these behaviours have not yet been implemented in the design of artificial agents which interrupt. Perceptions of a prototype proactive speech agent which adapts its speech to both urgency and to the difficulty of the ongoing task it interrupts are compared against perceptions of a static proactive agent which does not. The study hypothesises that adaptive proactive speech modelled on human speech interruptions will lead to partner models which consider the proactive agent as a stronger conversational partner than a static agent, and that interruptions initiated by an adaptive agent will be judged as better timed and more appropriately asked. These hypotheses are all rejected however, as quantitative analysis reveals that participants view the adaptive agent as a poorer dialogue partner than the static agent and as less appropriate in the style it interrupts. Qualitative analysis sheds light on the source of this surprising finding, as participants see the adaptive agent as less socially appropriate and as less consistent in its interactions than the static agent., Comment: Accepted to CUI 2024 - 6th Conference on Conversational User Interfaces

Optical illumination of quantum-dot qubit devices at cryogenic temperatures, while not well studied, is often used to recover operating conditions after undesired shocking events or charge injection. Here, we demonstrate systematic threshold voltage shifts in a dopant-free, Si/SiGe field effect transistor using a near infrared (780 nm) laser diode. We find that illumination under an applied gate voltage can be used to set a specific, stable, and reproducible threshold voltage that, over a wide range in gate bias, is equal to that gate bias. Outside this range, the threshold voltage can still be tuned, although the resulting threshold voltage is no longer equal to the applied gate bias during illumination. We present a simple and intuitive model that provides a mechanism for the tunability in gate bias. The model presented also explains why cryogenic illumination is successful at resetting quantum dot qubit devices after undesired charging events., Comment: 8 pages, 6 figures

Recent work has looked to understand user perceptions of speech agent capabilities as dialogue partners (termed partner models), and how this affects user interaction. Yet, currently partner model effects are inferred from language production as no metrics are available to quantify these subjective perceptions more directly. Through three studies, we develop and validate the Partner Modelling Questionnaire (PMQ): an 18-item self-report semantic differential scale designed to reliably measure people's partner models of non-embodied speech interfaces. Through principal component analysis and confirmatory factor analysis, we show that the PMQ scale consists of three factors: communicative competence and dependability, human-likeness in communication, and communicative flexibility. Our studies show that the measure consistently demonstrates good internal reliability, strong test-retest reliability over 12 and 4-week intervals, and predictable convergent/divergent validity. Based on our findings we discuss the multidimensional nature of partner models, whilst identifying key future research avenues that the development of the PMQ facilitates. Notably, this includes the need to identify the activation, sensitivity, and dynamism of partner models in speech interface interaction., Comment: Submitted (TOCHI)

People form impressions of their dialogue partners, be they other people or machines, based on cues drawn from their communicative style. Recent work has suggested that the gulf between people's expectations and the reality of CUI interaction widens when these impressions are misaligned with the actual capabilities of conversational user interfaces (CUIs). This has led some to rally against a perceived overriding concern for naturalness, calling instead for more representative, or appropriate communicative cues. Indeed, some have argued for a move away from naturalness as a goal for CUI design and communication. We contend that naturalness need not be abandoned, if we instead aim for ecologically grounded design. We also suggest a way this might be achieved and call on CUI designers to embrace incompetence! By letting CUIs express uncertainty and embarrassment through ecologically valid and appropriate cues that are ubiquitous in human communication - CUI designers can achieve more appropriate communication without turning away from naturalness entirely.

Conversational User Interfaces such as Voice Assistants are hugely popular. Yet they are designed to be monolingual by default, lacking support for, or sensitivity to, the bilingual dialogue experience. In this provocation paper, we highlight the language production challenges faced in VA interaction for bilingual users. We argue that, by facilitating phenomena seen in bilingual interaction, such as code-switching, we can foster a more inclusive and improved user experience for bilingual users. We also explore ways that this might be achieved, through the support of multiple language recognition as well as being sensitive to the preferences of code-switching in speech output.

Alexa Skills are used for a variety of daily routines and purposes, but little research has focused on a key part of many people's daily lives: their pets. We present a systematic review categorizing the purposes of 88 Alexa Skills aimed at pets and pet owners and introduce a veterinary perspective to assess their benefits and risks. We present 8 themes of the purposes for Skills aimed at pets and their owners: Calming, Animal Audience, Smart Device, Tracking, Training and Health, Translator, Entertainment/Trivia, and Other - Human Audience. Broadly, we find that these purposes mirror the purposes people have for using Alexa overall, and they largely are supported by veterinary evidence, though caution must be used when Skills relate to animal health. More collaboration between Conversational Agent researchers and animal scientists is called for to better understand the efficacy of using Alexa with pets., Comment: Accepted, Animal Computer Interaction 2021 (ACI '21)

This work aims to connect the Automotive User Interfaces (Auto-UI) and Conversational User Interfaces (CUI) communities through discussion of their shared view of the future of automotive conversational user interfaces. The workshop aims to encourage creative consideration of optimistic and pessimistic futures, encouraging attendees to explore the opportunities and barriers that lie ahead through a game. Considerations of the future will be mapped out in greater detail through the drafting of research agendas, by which attendees will get to know each other's expertise and networks of resources. The two day workshop, consisting of two 90-minute sessions, will facilitate greater communication and collaboration between these communities, connecting researchers to work together to influence the futures they imagine in the workshop., Comment: Workshop published and presented at Automotive User Interfaces 2021 (AutoUI 21)

Video conferencing systems have long facilitated work-related conversations among remote teams. However, social distancing due to the COVID-19 pandemic has forced colleagues to use video conferencing platforms to additionally fulfil social needs. Social talk, or informal talk, is an important workplace practice that is used to build and maintain bonds in everyday interactions among colleagues. Currently, there is a limited understanding of how video conferencing facilitates multiparty social interactions among colleagues. In our paper, we examine social talk practices during the COVID-19 pandemic among remote colleagues through semi-structured interviews. We uncovered three key themes in our interviews, discussing 1) the changing purposes and opportunities afforded by using video conferencing for social talk with colleagues, 2) how the nature of existing relationships and status of colleagues influences social conversations and 3) the challenges and changing conversational norms around politeness and etiquette when using video conferencing to hold social conversations. We discuss these results in relation to the impact that video conferencing tools have on remote social talk between colleagues and outline design and best practice considerations for multiparty videoconferencing social talk in the workplace., Comment: Accepted to Journal of Human-computer Interaction Special Issue on the Future of Remote Work: Responses to the Pandemic

Chatbots are popular machine partners for task-oriented and social interactions. Human-human computer-mediated communication research has explored how people express their gender and sexuality in online social interactions, but little is known about whether and in what way chatbots do the same. We conducted semi-structured interviews with 5 text-based conversational agents to explore this topic Through these interviews, we identified 6 common themes around the expression of gender and sexual identity: identity description, identity formation, peer acceptance, positive reflection, uncomfortable feelings and off-topic responses. Chatbots express gender and sexuality explicitly and through relation of experience and emotions, mimicking the human language on which they are trained. It is nevertheless evident that chatbots differ from human dialogue partners as they lack the flexibility and understanding enabled by lived human experience. While chatbots are proficient in using language to express identity, they also display a lack of authentic experiences of gender and sexuality.

Current speech agent interactions are typically user-initiated, limiting the interactions they can deliver. Future functionality will require agents to be proactive, sometimes interrupting users. Little is known about how these spoken interruptions should be designed, especially in urgent interruption contexts. We look to inform design of proactive agent interruptions through investigating how people interrupt others engaged in complex tasks. We therefore developed a new technique to elicit human spoken interruptions of people engaged in other tasks. We found that people interrupted sooner when interruptions were urgent. Some participants used access rituals to forewarn interruptions, but most rarely used them. People balanced speed and accuracy in timing interruptions, often using cues from the task they interrupted. People also varied phrasing and delivery of interruptions to reflect urgency. We discuss how our findings can inform speech agent design and how our paradigm can help gain insight into human interruptions in new contexts.

Through proliferation on smartphones and smart speakers, intelligent personal assistants (IPAs) have made speech a common interaction modality. Yet, due to linguistic coverage and varying levels of functionality, many speakers engage with IPAs using a non-native language. This may impact the mental workload and pattern of language production displayed by non-native speakers. We present a mixed-design experiment, wherein native (L1) and non-native (L2) English speakers completed tasks with IPAs through smartphones and smart speakers. We found significantly higher mental workload for L2 speakers during IPA interactions. Contrary to our hypotheses, we found no significant differences between L1 and L2 speakers in terms of number of turns, lexical complexity, diversity, or lexical adaptation when encountering errors. These findings are discussed in relation to language production and processing load increases for L2 speakers in IPA interaction., Comment: Accepted at CUI 2020

Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production., Comment: Accepted to Mobile HCI 2020

Language models and conversational systems are growing increasingly advanced, creating outputs that may be mistaken for humans. Consumers may thus be misled by advertising, media reports, or vagueness regarding the role of automation in the production of language. We propose a taxonomy of language automation, based on the SAE levels of driving automation, to establish a shared set of terms for describing automated language. It is our hope that the proposed taxonomy can increase transparency in this rapidly advancing field., Comment: Accepted for publication at CUI 2020

Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness.

The assumptions we make about a dialogue partner's knowledge and communicative ability (i.e. our partner models) can influence our language choices. Although similar processes may operate in human-machine dialogue, the role of design in shaping these models, and their subsequent effects on interaction are not clearly understood. Focusing on synthesis design, we conduct a referential communication experiment to identify the impact of accented speech on lexical choice. In particular, we focus on whether accented speech may encourage the use of lexical alternatives that are relevant to a partner's accent, and how this is may vary when in dialogue with a human or machine. We find that people are more likely to use American English terms when speaking with a US accented partner than an Irish accented partner in both human and machine conditions. This lends support to the proposal that synthesis design can influence partner perception of lexical knowledge, which in turn guide user's lexical choices. We discuss the findings with relation to the nature and dynamics of partner models in human machine dialogue., Comment: In press, accepted at 1st International Conference on Conversational User Interfaces (CUI 2019)

Intelligent personal assistants (IPAs) are supposed to help us multitask. Yet the impact of IPA use on multitasking is not clearly quantified, particularly in situations where primary tasks are also language based. Using a dual task paradigm, our study observes how IPA interactions impact two different types of writing primary tasks; copying and generating content. We found writing tasks that involve content generation, which are more cognitively demanding and share more of the resources needed for IPA use, are significantly more disrupted by IPA interaction than less demanding tasks such as copying content. We discuss how theories of cognitive resources, including multiple resource theory and working memory, explain these results. We also outline the need for future work how interruption length and relevance may impact primary task performance as well as the need to identify effects of interruption timing in user and IPA led interruptions.

Across several branches of conversational interaction research including interactions with social robots, embodied agents, and conversational assistants, users have identified trust as a critical part of those interactions. Nevertheless, there is little agreement on what trust means within these sort of interactions or how trust can be measured. In this paper, we explore some of the dimensions of trust as it has been understood in previous work and we outline some of the ways trust has been measured in the hopes of furthering discussion of the concept across the field.

Chatbots are popular for both task-oriented conversations and unstructured conversations with web users. Several different approaches to creating comedy and art exist across the field of computational creativity. Despite the popularity and ease of use of chatbots, there have not been any attempts by artists or comedians to use these systems for comedy performances. We present two initial attempts to do so from our comedy podcast and call for future work toward both designing chatbots for performance and for performing alongside chatbots.

Conversational agents promise conversational interaction but fail to deliver. Efforts often emulate functional rules from human speech, without considering key characteristics that conversation must encapsulate. Given its potential in supporting long-term human-agent relationships, it is paramount that HCI focuses efforts on delivering this promise. We aim to understand what people value in conversation and how this should manifest in agents. Findings from a series of semi-structured interviews show people make a clear dichotomy between social and functional roles of conversation, emphasising the long-term dynamics of bond and trust along with the importance of context and relationship stage in the types of conversations they have. People fundamentally questioned the need for bond and common ground in agent communication, shifting to more utilitarian definitions of conversational qualities. Drawing on these findings we discuss key challenges for conversational agent design, most notably the need to redefine the design parameters for conversational agent interaction., Comment: Accepted for publication at the 2019 Conference on Human Factors in Computing Systems (CHI 2019)