从语音到空间：利用增强现实进行共享视图中话语的定位
From Speech-to-Spatial: Grounding Utterances on A Live Shared View with Augmented Reality
AI总结：
本文提出Speech-to-Spatial框架，通过语音指令生成空间定位的AR指导，无需额外线索或人工标注，提升任务效率和可用性。
摘要：
我们介绍了Speech-to-Spatial，一种参照消歧框架，将口头远程协助指令转换为空间定位的AR指导。不同于依赖额外线索（如手势、注视）或手动专家标注的先前系统，Speech-to-Spatial仅通过语音参考（语音输入）推断目标。受我们对语音参照模式的形成研究启发，我们表征了人们指定目标的常见方式（直接属性、关系、回忆和链式），并将它们映射到我们以对象为中心的关系图中。给定一个陈述，参照线索被解析并渲染为持久的现场AR视觉指导，减少远程指导中的迭代微指导（“稍微向右一点”，“现在停止”）。我们通过远程指导协助和意图消歧场景展示了系统用例。我们的评估显示，Speech-to-Spatial相比传统语音-only基线，提高了任务效率，减少了认知负荷，并增强了可用性，将脱离身体的口头指令转化为在共享视图上可视觉解释和执行的指导。
英文摘要：
We introduce Speech-to-Spatial, a referent disambiguation framework that converts verbal remote-assistance instructions into spatially grounded AR guidance. Unlike prior systems that rely on additional cues (e.g., gesture, gaze) or manual expert annotations, Speech-to-Spatial infers the intended target solely from spoken references (speech input). Motivated by our formative study of speech referencing patterns, we characterize recurring ways people specify targets (Direct Attribute, Relational, Remembrance, and Chained) and ground them to our object-centric relational graph. Given an utterance, referent cues are parsed and rendered as persistent in-situ AR visual guidance, reducing iterative micro-guidance ("a bit more to the right", "now, stop.") during remote guidance. We demonstrate the use cases of our system with remote guided assistance and intent disambiguation scenarios. Our evaluation shows that Speechto-Spatial improves task efficiency, reduces cognitive load, and enhances usability compared to a conventional voice-only baseline, transforming disembodied verbal instruction into visually explainable, actionable guidance on a live shared view.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
