Speaking aloud to a wearable AR assistant in public can be socially awkward, and re-articulating the same requests every day creates unnecessary effort. We present SpeechLess, a wearable AR assistant that introduces a speech-based intent granularity control paradigm grounded in personalized spatial memory. SpeechLess helps users "speak less," while still obtaining the information they need, and supports gradual explicitation of intent when more complex expression is required. SpeechLess binds prior interactions to multimodal personal context-space, time, activity, and referents-to form spatial memories, and leverages them to extrapolate missing intent dimensions from under-specified user queries. This enables users to dynamically adjust how explicitly they express their informational needs, from full-utterance to micro/zero-utterance interaction. We motivate our design through a week-long formative study using a commercial smart glasses platform, revealing discomfort with public voice use, frustration with repetitive speech, and hardware constraints. Building on these insights, we design SpeechLess, and evaluate it through controlled lab and in-the-wild studies. Our results indicate that regulated speech-based interaction, can improve everyday information access, reduce articulation effort, and support socially acceptable use without substantially degrading perceived usability or intent resolution accuracy across diverse everyday environments.

翻译：在公共场合对可穿戴AR助手大声说话可能带来社交尴尬，而每天重复表达相同请求则造成不必要的负担。我们提出SpeechLess——一种基于个性化空间记忆、引入语音意图粒度控制范式的可穿戴AR助手。SpeechLess帮助用户"少说话"的同时仍能获取所需信息，并在需要更复杂表达时支持意图的渐进显式化。该工具将先前的交互与多模态个人上下文（空间、时间、活动及所指对象）绑定形成空间记忆，并利用这些记忆从用户不充分的查询中推断缺失的意图维度。这使得用户能够动态调整表达信息需求的显式程度，从完整话语交互到微话语/零话语交互均可实现。我们通过为期一周的商用智能眼镜平台形成性研究来论证设计动机，揭示了用户对公共语音使用的抵触、对重复语音的挫败感以及硬件限制。基于这些洞察，我们设计了SpeechLess，并通过受控实验室与实地研究进行评估。结果表明，调节式语音交互能改善日常信息获取、降低表达负担，并在多样化日常环境中保持社交可接受性，且不会显著降低感知可用性或意图解析准确率。