VQV Signal
Real-time voice agents have transitioned from prototypes to production, but evaluation methods remain fragmented across speech modeling, psycholinguistics, and agentic benchmarks. Different fields focus on latency, prediction accuracy, or task success, making unified assessment difficult.
Real-time voice agents have transitioned from prototypes to production, but evaluation methods remain fragmented across speech modeling, psycholinguistics, and agentic benchmarks. Different fields focus on latency, prediction accuracy, or task success, making unified assessment difficult.
AI-assisted summary based on the listed source.
Real-time voice agents have moved from research prototypes to production deployments, yet the literature describing them is fragmented across three communities that rarely cite one another: speech foundation modelling, turn-taking psycholinguistics, and agentic evaluation. Architecture papers report latency,...
A cohesive evaluation framework is needed to accurately measure real-time voice agent performance and improve their deployment. Bridging these disciplinary gaps can lead to more effective and reliable voice agent systems.
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VQV surfaced this signal because it is recent, relevant to AI Voice, connected to arXiv.
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