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, partner model effects are currently inferred from language production as no metrics are available to quantify these subjective perceptions more directly. Through three phases of work, 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 confirmatory factor analysis, we confirm 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 4- and 12-week intervals, and predictable convergent/divergent validity. Based on our findings, we discuss the multidimensional nature of partner models, while 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.

Advances in natural language processing and understanding have led to a rapid growth in the popularity of conversational user interfaces (CUIs). While CUIs introduce novel benefits, they also yield risks that may exploit people’s trust. Although research looking at unethical design deployed through graphical user interfaces (GUIs) established a thorough understanding of so-called dark patterns, there is a need to continue this discourse within the CUI community to understand potentially problematic interactions. Addressing this gap, we interviewed 27 participants from three cohorts: researchers, practitioners, and frequent users of CUIs. Applying thematic analysis, we construct five themes reflecting each cohort’s insights about ethical design challenges and introduce the CUI Expectation Cycle, bridging system capabilities and user expectations while considering each theme’s ethical caveats. This research aims to inform future development of CUIs to consider ethical constraints while adopting a human-centred approach.

Cycling continues to grow in popularity, both as a means to commute and for exercise. While there is a plethora of research studying technology use in vehicular travel, cycling remains a relatively understudied area—especially within HCI. We conducted an ethnography, adopting an ethnomethodological lens, to study cyclists as they use their bicycles for routine purposes. Through the use of a handlebar-mounted 360-degree action video camera, we conducted our study longitudinally with participants over a number of weeks. Our analysis explicates our participants accountable use of different electronic technologies while on the go and in this paper we present four fragments of their use of different technologies as exemplars from our corpus. Our paper offers insights into the use of technology on bicycles, including how cyclists select moments of opportunity to use technology for different purposes. We conclude by offering design implications for the design of interactive technologies for cyclists.

The increased popularity of CUIs has motivated HCI work around specific approaches to research, design, and implementation, while also reflecting on these topics. However, current research is highly fragmented and lacks critical mass around topics such as theory, methods and design. Building this critical mass is a fundamentally multidisciplinary endeavour. CUIs involve language based interaction, either through speech or text, with another agent(s) or device(s). This type of interaction not only needs to engage with traditional HCI approaches, but also to embrace methods from communicative and social sciences. This is crucial for making progress towards human-centred conversational interfaces. Along with the recent ACM SIGCHI Conversational User Interfaces conference (ACM CUI), this special issue showcases research to further solidify the foundations of the field in these areas. Below we outline some key challenges faced by the field, describe the papers in this special issue, and then outline areas for future research.

Speech-enabled smart speakers are common devices used for numerous tasks in everyday life. While speech-enabled technologies are widespread, using one’s voice as a computing modality introduces new accessibility challenges for people with speech disfluencies such as stammering (also known as stuttering). This paper investigates the smart speaker user experiences of people who stammer over three weeks. We conducted diary studies and semi-structured interviews with 11 individuals to identify their daily routines, difficulties with successful interactions, and strategies to overcome these barriers. Our analysis demonstrates key factors such as device location, its affordances, and the structure of commands had a strong impact on user experience. Participants highlighted different linguistic strategies to try and overcome interaction difficulties and discussed the potential of using smart speakers for speech and language therapy. We emphasise the need to further understand the experiences of people who stammer in smart speaker design to increase their accessibility.

Voice-based Conversational Agents (CAs) are increasingly being used by children. Through a review of 38 research papers, this work maps trends, themes, and methods of empirical research on children and CAs in HCI research over the last decade. A thematic analysis of the research found that work in this domain focuses on seven key topics: ascribing human-like qualities to CAs, CAs? support of children?s learning, the use and role of CAs in the home and family context, CAs? support of children?s play, children?s storytelling with CA, issues concerning the collection of information revealed by CAs, and CAs designed for children with differing abilities. Based on our findings, we identify the needs to account for children’s intersectional identities and linguistic and cultural diversity and theories from multiple disciples in the design of CAs, develop heuristics for child-centric interaction with CAs, to investigate implications of CAs on social cognition and interpersonal relationships, and to examine and design for multi-party interactions with CAs for different domains and contexts.

The goal of the seventh edition of SCAI (https://scai.info) is to bring together and further grow a community of researchers and practitioners interested in conversational systems for information access. The previous iterations of the workshop already demonstrated the breadth and multidisciplinarity inherent in the design and development of conversational search agents. The proposed shift from traditional web search to search interfaces enabled via human-like dialogue leads to a number of challenges, and although such challenges have received more attention in the recent years, there are many pending research questions that should be addressed by the information retrieval community and can largely benefit from a collaboration with other research fields, such as natural language processing, machine learning, human-computer interaction and dialogue systems. This workshop is intended as a platform enabling a continuous discussion of the major research challenges that surround the design of search-oriented conversational systems. This year, participants have the opportunity to meet in person and have more in-depth interactive discussions with a full-day onsite workshop.

Casual conversation, where people engage in social talk or phatic communion (Coupland et al., 1992) (i.e., non-task oriented talk) is acknowledged as important in facilitating collaboration among c...

Conversational agents (CAs) are systems that interact with humans through natural language user interfaces. They include systems with a range of conversational capabilities and modalities. For example, there are text- or voice-only question-answering interactions such as Apple Siri, Google Assistant, and Amazon Alexa, and there are also multimodal conversational AI agents that can engage users in long-term dialogues. Advances in speech recognition, natural language processing, and computer vision have resulted in a greater acceptance and use of CAs. CAs have already started to play important roles in various healthcare settings, including assisting clinicians during consultations, assisting consumers in changing health behaviours, and helping patients such as the elderly in their living environments. \n \nThere have been several systematic reviews on the use of CAs in health and wellbeing recently. Although the field still appears to be nascent, the emerging evidence has shown user acceptance of CAs in the healthcare domain as well as the early promises in boosting healthcare outcomes in both physical and mental health. Despite the increasing adoption and the benefits of using CAs to support health and wellbeing, the review studies also revealed (i) patient safety was rarely examined, (ii) health outcomes were inadequately measured, and (iii) no standardised evaluation methods were employed. There were also limitations in reporting the technical implementation details of CAs used, making the replicability of prior studies problematic. \n \nIn addition to addressing some of the current challenges and limitations, this special issue features cutting-edge research on designing, developing, and evaluating CAs for health and wellbeing that aim to improve health outcomes and services, and satisfy unique application needs (e.g., safety, trust, and user experience). The seven articles included in this special issue cover many application areas ranging from mental health and social support to information seeking to coaching. Amongst the accepted articles, mental health and social support themes represented the primary research foci. The articles also covered different population groups including older adults, young adults, and homeless people. Based on their foci, we have grouped the articles in this special issue by three areas: mental health, older adult wellbeing, and social support and coaching.

Perceptions of system competence and communicative ability, termed partner models, play a significant role in speech interface interaction. Yet we do not know what the core dimensions of this concept are. Taking a psycholexical approach, our paper is the first to identify the key dimensions that define partner models in speech agent interaction. Through a repertory grid study (N=21), a review of key subjective questionnaires, an expert review of resulting word pairs and an online study of 356 users of speech interfaces, we identify three key dimensions that make up a users’ partner model: 1) perceptions towards partner competence and dependability; 2) assessment of human-likeness; and 3) a system’s perceived cognitive flexibility. We discuss the implications for partner modelling as a concept, emphasising the importance of salience and the dynamic nature of these perceptions.

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.

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.

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.

Abstract Speech interfaces are growing in popularity. Through a review of 99 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in the field of human–computer interaction (HCI). We find that studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes or developed systems. Measuring task and interaction was common, as was using self-report questionnaires to measure concepts like usability and user attitudes. A thematic analysis of the research found that speech HCI work focuses on nine key topics: system speech production, design insight, modality comparison, experiences with interactive voice response systems, assistive technology and accessibility, user speech production, using speech technology for development, peoples’ experiences with intelligent personal assistants and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably taking into account technological advancements, the need to develop theories of speech interface interaction, grow critical mass in this domain, increase design work and expand research from single to multiple user interaction contexts so as to reflect current use contexts. We also highlight the need to improve measure reliability, validity and consistency, in the wild deployment and reduce barriers to building fully functional speech interfaces for research. RESEARCH HIGHLIGHTS Most papers focused on usability/theory-based or wider system experience research with a focus on Wizard of Oz and developed systems Questionnaires on usability and user attitudes often used but few were reliable or validated Thematic analysis showed nine primary research topics Challenges identified in theoretical approaches and design guidelines, engaging with technological advances, multiple user and in the wild contexts, critical research mass and barriers to building speech interfaces