Despite significant improvements in automatic speech recognition and spoken language understanding - human interaction with Virtual Personal Assistants (VPAs) through speech remains irregular and sporadic. According to recent studies, currently the usage of VPAs is constrained to basic tasks such as checking facts, playing music, and obtaining weather updates.In this paper, we present results of a survey (N = 118) that analyses usage of VPAs by frequent and infrequent users. We investigate how usage experience, performance expectations, and privacy concerns differ between these two groups. The results indicate that, compared with infrequent users, frequent users of VPAs are more satisfied with their assistants, more eager to use them in a variety of settings, yet equally concerned about their privacy.
Intelligent Personal Assistants (IPAs) are widely available on devices such as smartphones. However, most people do not use them regularly. Previous research has studied the experiences of frequent IPA users. Using qualitative methods we explore the experience of infrequent users: people who have tried IPAs, but choose not to use them regularly. Unsurprisingly infrequent users share some of the experiences of frequent users, e.g. frustration at limitations on fully hands-free interaction. Significant points of contrast and previously unidentified concerns also emerge. Cultural norms and social embarrassment take on added significance for infrequent users. Humanness of IPAs sparked comparisons with human assistants, juxtaposing their limitations. Most importantly, significant concerns emerged around privacy, monetization, data permanency and transparency. Drawing on these findings we discuss key challenges, including: designing for interruptability; reconsideration of the human metaphor; issues of trust and data ownership. Addressing these challenges may lead to more widespread IPA use. Voice-controlled intelligent personal assistants, such as Cortana, Google Now, Siri and Alexa, are increasingly becoming a part of users' daily lives, especially on mobile devices. They introduce a significant change in information access, not only by introducing voice control and touch gestures but also by enabling dialogues where the context is preserved. This raises the need for evaluation of their effectiveness in assisting users with their tasks. However, in order to understand which type of user interactions reflect different degrees of user satisfaction we need explicit judgements. In this paper, we describe a user study that was designed to measure user satisfaction over a range of typical scenarios of use: controlling a device, web search, and structured search dialogue. Using this data, we study how user satisfaction varied with different usage scenarios and what signals can be used for modeling satisfaction in the different scenarios. We find that the notion of satisfaction varies across different scenarios, and show that, in some scenarios (e.g. making a phone call), task completion is very important while for others (e.g. planning a night out), the amount of effort spent is key. We also study how the nature and complexity of the task at hand affects user satisfaction, and find that preserving the conversation context is essential and that overall task-level satisfaction cannot be reduced to query-level satisfaction alone. Finally, we shed light on the relative effectiveness and usefulness of voice-controlled intelligent agents, explaining their increasing popularity and uptake relative to the traditional query-response interaction.