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Inquesto Score: A reliability Protocol For Voice Agents

arXiv 2609.30514 ↗First posted 24 Sep 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Voice agents are increasingly deployed in workflows where failed interactions can affect transactions, access, and other consequential outcomes, creating a need for reproducible and interpretable evaluation. We introduce Inquesto Score (IS), a protocol for measuring voice-agent reliability as the percentage of calls in a fixed, versioned evaluation population that achieve the caller's goal without a functional failure or worse. Rather than combining heterogeneous metrics, IS defines explicit failure events and severity levels and evaluates the deployed voice pipeline. Timing failures, including talk-over and delayed responses, are measured directly from audio, while semantic and state-dependent failures are evaluated using scenario predicates, tool traces, and a pinned open-model judge. Diagnostic views of behavior, acoustic robustness, identity handling, and speaker groups accompany the score without being combined into it. Inquesto Score v0.1 evaluates 30 scenarios, three acoustic conditions, four speaker groups, and 306 calls per agent across 13 configurations of a reference voice-agent system. Our evaluation shows that reliable measurement requires evidence beyond transcripts, explicit treatment of deployment conditions, and validation of the evaluators used to determine outcomes. We release the protocol, reference implementation, and evaluation records.

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References · 13

  1. 2024Qwen2.5 Technical Reportcited by 4,942
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  3. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  4. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  5. 2022ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wildcited by 121
  6. 2025Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilitiescited by 100
  7. 2022Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkitcited by 97
  8. 2025Position: Evaluating Generative AI Systems Is a Social Science Measurement Challengecited by 63
  9. 2026EVA-Bench: A New End-to-end Framework for Evaluating Voice Agentscited by 18
  10. 2026VAmoS Bench: Voice Agent Simulation Benchcited by 7
  11. 2025SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditionscited by 6
  12. 2025VoiceAgentEval: A Dual-Dimensional Benchmark for Expert-Level Intelligent Voice-Agent Evaluation of Xbench's Professional-Aligned Seriescited by 5
1 more
  1. 2026MTVA-Bench: Evaluating the Language Model Inside Cascaded Voice Agentscited by 3

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