To this day, turn-taking models determining voice agents' conduct have been examined from a technical point of view, while the interactional constraints or resources they constitute for human conversationalists have not been empirically described. From the detailed analysis of corpora of naturalistic data, we document how, whether in interaction with rule-based robots from a 'pre-LLM era' or with the most recent voice agents, humans' conduct was produced in reference to the ever-present risk that, each time they spoke, their talk may trigger a new uncalled-for contribution from the artificial agent. We argue that this 'omnirelevance of human speech' is a constitutive feature of current human-agent interaction that, due to recent improvements in voice capture technology, weighs on human practices even more today than in the past. Specifically, we document how, in multiparty settings, humans shaped their conduct in such a way as to remain undetected by the machine's sensors.

翻译：迄今为止，决定语音助手行为的轮次转换模型多从技术角度被检视，而它们对人类对话者构成的交互约束或资源尚未得到实证描述。通过对自然语料库的细部分析，我们记录了人类在与“前大语言模型时代”的基于规则机器人或最新语音助手互动时，其行为如何始终参照着一种无处不在的风险而产生：即每次发言都可能触发人工智能体做出新的、非预期的回应。我们认为这种“人类语音的全相关性”是当前人机交互的构成性特征，并且由于语音捕捉技术的近期改进，其对人类实践的影响甚至比过去更为显著。具体而言，我们记录了在多参与者场景中，人类如何通过调整自身行为以规避机器传感器的侦测。