Speech interfaces are growing in popularity. Through a review of 68 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in HCI. We find that most studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes, or developed systems by 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, modality comparison, user speech production, assistive technology & accessibility, design insight, experiences with interactive voice response (IVR) systems, using speech technology for development, people's experiences with intelligent personal assistants (IPAs) and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably 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. Author Keywords Speech interfaces; speech HCI; review; speech technology; voice user interfaces Research Highlights• Most papers focused on usability/theory-based or wider system experience research with a focus on Wizard of Oz and developed systems, though a lack of design work • Questionnaires on usability and user attitudes often used but few were reliable or validated • Thematic analysis showed nine primary research topics • Gaps in research critical mass, speech HCI theories, and multiple user contexts

Modeling, analysis and synthesis of behaviour are the subject of major efforts in computing science, especially when it comes to technologies that make sense of humanhuman and human-machine interactions. This article outlines some of the most important issues that still need to be addressed to ensure substantial progress in the field, namely 1) development and adoption of virtuous data collection and sharing practices, 2) shift of the focus of interest from individuals to dyads and groups, 3) endowment of artificial agents with internal representations of users and context, 4) modeling of cognitive and semantic processes underlying social behaviour, and 5) identification of application domains and strategies for moving from laboratory to the realworld products.

Automatic prediction of engagement in human-human and human-machine dyadic and multiparty interaction scenarios could greatly aid in evaluation of the success of communication. A corpus of eight face-to-face dyadic casual conversations was recorded and used as the basis for an engagement study, which examined the effectiveness of several methods of engagement level recognition. A convolutional neural network based analysis was seen to be the most effective.

In this paper we present the AmuS database of about three hours worth of data related to amused speech recorded from two males and one female subjects and contains data in two languages French and English. We review previous work on smiled speech and speech-laughs. We describe acoustic analysis on part of our database, and a perception test comparing speech-laughs with smiled and neutral speech. We show the efficiency of the data in AmuS for synthesis of amused speech by training HMM-based models for neutral and smiled speech for each voice and comparing them using an on-line CMOS test.

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