The rise of generative AI has increased attention to voice interfaces. In immersive analytics, we conceptualize this trend as Speech-driven Immersive Analytics. While speech interfaces enable natural interactions, users, especially novices, still face a learning curve in articulating analytic intent and exploring data during the foraging phase. Prior work has primarily addressed these challenges through multimodal interaction or textual disambiguation. We introduce a context-aware Speech-driven Immersive Analytics framework (SIA) as a speech-oriented approach that leverages speech acts to convey actionable intent. This framework (SIA) was designed based on a formative study, a prototype development, three technical studies, and a user study. By extracting speech acts from utterances, SIA infers analytic tasks and embodiment tendencies, then integrates them with spatial, chart, and data context to generate feedforward: previews of potential actions and outcomes. The formative study identified user needs. The technical studies demonstrated that SIA improved the inference quality, enabling context-aware feedforward generation. The user study highlighted that the SIA-based prototype was responsive and intuitive, and feedforward helped users learn during the onboarding phase of data exploration. In particular, the user study identified which feedforward elements participants referenced and how they applied them when expressing intent in immersive analytics. Our key technical findings emphasize that the ensemble model, embedded in the Uncertainty Estimator, improves accuracy and stabilizes task inference. The Projector’s context summary was critical in generating context-aware feedforward. Based on these results, we discuss future research directions for intelligent Speech-driven Immersive Analytics.

Embodiment shapes how users verbally express intent when interacting with data through speech interfaces in immersive analytics. Despite growing interest in Natural Language Interactions (NLIs) for visual analytics in immersive environments, users' speech patterns and their use of embodiment cues in speech remain underexplored. Understanding their interplay is crucial to bridging the gap between users' intent and an immersive analytic system. To address this, we report the results from 15 participants in a user study conducted using the Wizard of Oz method. We performed axial coding on 1,280 speech acts derived from 734 utterances, examining how analysis tasks are carried out with embodiment and linguistic features. Next, we measured Speech Input Uncertainty for each analysis task using the semantic entropy of utterances, estimating how uncertain users' speech inputs appear to an analytic system. Through these analyses, we identified five speech input patterns, showing that users dynamically blend embodied and non-embodied speech acts depending on data analysis tasks, phases, and Embodiment Reliance driven by the counts and types of embodiment cues in each utterance. We then examined how these patterns align with user reflections on factors that challenge speech interaction during the study. Finally, we propose design implications aligned with the five patterns.

Visualization designers (e.g., journalists or data analysts) often rely on examples to explore the space of possible designs, yet we have little insight into how examples shape data visualization design outcomes. While the effects of examples have been studied in other disciplines, such as web design or engineering, the results are not readily applicable to visualization due to inconsistencies in findings and challenges unique to visualization design. Towards bridging this gap, we conduct an exploratory experiment involving 32 data visualization designers focusing on the influence of five factors (timing, quantity, diversity, data topic similarity, and data schema similarity) on objectively measurable design outcomes (e.g., numbers of designs and idea transfers). Our quantitative analysis shows that when examples are introduced after initial brainstorming, designers curate examples with topics less similar to the dataset they are working on and produce more designs with a high variation in visualization components. Also, designers copy more ideas from examples with higher data schema similarities. Our qualitative analysis of participants' thought processes provides insights into why designers incorporate examples into their designs, revealing potential factors that have not been previously investigated. Finally, we discuss how our results inform how designers may use examples during design ideation as well as future research on quantifying designs and supporting example-based visualization design. All supplemental materials are available in our OSF repo.