Recent chart-authoring systems, such as Amazon Q in QuickSight and Copilot for Power BI, demonstrate an emergent focus on supporting natural language input to share meaningful insights from data through chart creation. Currently, chart-authoring systems tend to integrate voice input capabilities by relying on speech-to-text transcription, processing spoken and typed input similarly. However, cross-modality input comparisons in other interaction domains suggest that the structure of spoken and typed-in interactions could notably differ, reflecting variations in user expectations based on interface affordances. Thus, in this work, we compare spoken and typed instructions for chart creation. Findings suggest that while both text and voice instructions cover chart elements and element organization, voice descriptions have a variety of command formats, element characteristics, and complex linguistic features. Based on these findings, we developed guidelines for designing voice-based authoring-oriented systems and additional features that can be incorporated into existing text-based systems to support speech modality.

翻译：近期图表制作系统，如Amazon Q in QuickSight与Copilot for Power BI，展现出对支持自然语言输入以通过图表创建从数据中传递有意义洞察的明确关注。当前图表制作系统通常依赖语音转文字转录来整合语音输入能力，从而以相似方式处理口头与键入输入。然而，其他交互领域中跨模态输入比较表明，口头与键入交互的结构可能存在显著差异，反映出用户根据界面功能对期望的差异。因此，本研究对图表制作中的口头与键入指令进行了比较。研究结果表明，尽管文本和语音指令均涉及图表元素及元素组织，但语音描述具有多种命令格式、元素特征及复杂语言特性。基于这些发现，我们制定了面向语音的图表制作系统设计指南，并提出了可集成至现有文本系统中以支持语音模态的附加功能。