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. CCS Concepts • Human-centered computing → User studies; Natural language interfaces; Accessibility design and evaluation methods.
Dasher is a promising fast assistive gaze communication method. However, previous evaluations of Dasher have been inconclusive. Either the studies have been too short, involved too few participants, suffered from sampling bias, lacked a control condition, used an inappropriate language model, or a combination of the above. To rectify this, we report results from two new evaluations of Dasher carried out using a Tobii P10 assistive eye-tracker machine. We also present a method of modifying Dasher so that it can use a state-of-the-art long-span statistical language model. Our experimental results show that compared to a baseline eye-typing method, Dasher resulted in significantly faster entry rates (12.6 wpm versus 6.0 wpm in Experiment 1, and 14.2 wpm versus 7.0 wpm in Experiment 2). These faster entry rates were possible while maintaining error rates comparable to the baseline eye-typing method. Participants' perceived physical demand, mental demand, effort and frustration were all significantly lower for Dasher. Finally, participants significantly rated Dasher as being more likeable, requiring less concentration and being more fun.
Abstract-The Experience Sampling Method (ESM) captures participants' thoughts and feelings in their everyday environments. Mobile and wearable technologies afford us opportunities to reach people using ESM in varying contexts. However, a lack of programming knowledge often hinders researchers in creating ESM applications. In practice, they rely on specialised tools for app creation. Our initial review of these tools indicates that most are expensive commercial services, and none utilise the full potential of sensors for creating context-aware applications. We present "Jeeves", a visual language to facilitate ESM application creation. Inspired by successful visual languages in literature, our block-based notation enables researchers to visually construct ESM study specifications. We demonstrate its applicability by replicating existing ESM studies found in medical and psychology literature. Our preliminary study with 20 participants demonstrates that both non-programmers and programmers are able to successfully utilise Jeeves. We discuss future work in extending Jeeves with alternative mobile technologies.
Through smartphones and smart speakers, intelligent personal assistants (IPAs) have made speech a common interaction modality. With linguistic coverage and varying functionality levels, many speakers engage with IPAs using a non-native language. This may impact mental workload and patterns of language production used by non-native speakers. We present a mixed-design experiment, where native (L1) and non-native (L2) English speakers completed tasks with IPAs via smartphones and smart speakers. We found significantly higher mental workload for L2 speakers in IPA interactions. Contrary to our hypotheses, we found no significant differences between L1 and L2 speakers in 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. CCS CONCEPTS • Human-centered computing → User studies; Natural language interfaces; HCI theory, concepts and models.
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