Everything counts: the managed omnirelevance of speech in 'human - voice agent' interaction

Abstract: 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.