# Everything Counts: The Managed Omnirelevance of Speech in Human-Voice Agent Interaction

arXiv: https://arxiv.org/abs/2510.22610 · HTML: https://mlsanity.com/paper/2510.22610/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 176 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 12 of 12)

- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024)
- [Multilingual Turn-taking Prediction Using Voice Activity Projection](https://mlsanity.com/paper/2403.06487/) (2024)
- [How Much Does Prosody Help Turn-taking? Investigations using Voice Activity Projection Models](https://mlsanity.com/paper/2209.05161/) (2022)
- [Real-time and Continuous Turn-taking Prediction Using Voice Activity Projection](https://mlsanity.com/paper/2401.04868/) (2024)
- [From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?](https://mlsanity.com/paper/2404.16047/) (2024)
- [The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error](https://mlsanity.com/paper/2009.01812/) (2020)
- [Towards Data-efficient Modeling for Wake Word Spotting](https://mlsanity.com/paper/2010.06659/) (2020)
- [Applying General Turn-taking Models to Conversational Human-Robot Interaction](https://mlsanity.com/paper/2501.08946/) (2025)
- [Large Language Models Know What To Say But Not When To Speak](https://mlsanity.com/paper/2410.16044/) (2024)
- [How UX Practitioners Produce Findings in Usability Testing](https://mlsanity.com/paper/1810.01007/) (2018)
- ["Playing the robot's advocate": Bystanders' descriptions of a robot's conduct in public settings](https://mlsanity.com/paper/2503.16889/) (2025)

## Abstract (arXiv)

To this day, turn-taking models determining voice agents' conduct have been examined primarily from a technical point of view, while the ways in which they emerge as interactional constraints or resources for human conversationalists in situ remain underexplored. Drawing on a detailed analysis of corpora of naturalistic data, we document how humans' conduct was produced in reference to the ever-present risk that, each time they spoke, their talk might trigger a new uncalled-for contribution from the artificial agent. We examine this phenomenon in interactions involving rule-based robots from a 'pre-LLM era' as well as the most recent voice agents. This 'omnirelevance of human speech' (i.e., the possibility that a conversational agent may erroneously respond to any speech it detects) emerged as a constitutive feature of these human-agent encounters. We describe some of the practices through which humans managed these artificial agents' turn-taking conduct. Given recent improvements in voice capture technology, we ask whether this 'omnirelevance of human speech' weighs even more heavily on human practices today than in the past.