Almost Human, Except When It Matters: VoxParity and the Decisions a Voice Should Change
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Abstract (arXiv)
A voice agent can handle almost every call on the words alone and still fail the few its sector's rules were written for. Emergency-call standards, fraud guidance, radio phraseology and vulnerability rules recognise that how a caller sounds, or what else is audible, can change the right action. VoxParity tests whether agents act on it. In 183 scenarios from 14 sectors, one transcript stays fixed while the audio changes (a coaching voice, a medical monitor beeping, a mayday under a radio check, noise over a drug name, a child's voice placing a bet, a frightened whisper), and with it the correct typed tool call. A words-only null test credits a system only if hearing the call moves its actions more than it moves a pipeline that only reads the words. Only 11 of the 23 systems that can also be run on the transcript pass. Descriptively, errors run toward the words: when the audio calls for protection, all 28 systems carry out the routine request more often than they over-react on clean calls (41% against 12% pooled; the words-only pipeline, 58% against 15%). Exploratory analyses place most of the leading systems' misses on cues they heard; systems beat the null almost entirely on items that state the rule; the leading systems overrule heard resignation or confusion far more often than acute alarm; and, in the models tested, describing the voice and stating the rule each recover part of the shortfall, leaving a gap on emotion.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 64 of its 92 references that are in the map. First posted on arXiv 28 Sep 2026.
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References · 64
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
- 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
- 2024Are We on the Right Way for Evaluating Large Vision-Language Models?cited by 893
- 2018Annotation Artifacts in Natural Language Inference Datacited by 656
- 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 651
- 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 428
- 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 295
- 2018Hypothesis Only Baselines in Natural Language Inferencecited by 291
- 2024MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmarkcited by 281
- 2023How is ChatGPT's behavior changing over time?cited by 265
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- 2021Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Testscited by 77
- 2025MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligencecited by 59
- 2025AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommonscited by 31
- 2026τ-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domainscited by 30
- 2026Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluencycited by 29
- 2025Do Audio LLMs Really LISTEN, or Just Transcribe? Measuring Lexical vs. Acoustic Emotion Cues Reliancecited by 23
- 2025VoiceAgentBench: Are Voice Assistants ready for agentic tasks?cited by 15
- 2025Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Datacited by 10
- 2025EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Modelscited by 10
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- 2026Audio2Tool: Speak, Call, Act -- A Dataset for Benchmarking Speech Tool Usecited by 7
- 2026Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadoxcited by 7
- 2026VAmoS Bench: Voice Agent Simulation Benchcited by 6
- 2026VoxSafeBench: Not Just What Is Said, but Who, How, and Wherecited by 5
- 2026ParaBridge: Bridging Paralinguistic Perception and Dialogue Behavior in Speech Language Modelscited by 4
- 2025What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channelscited by 3
- 2026VoxPrivacy: A Benchmark for Evaluating Interactional Privacy of Speech Language Modelscited by 3
- 2026From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Benchcited by 3
- 2026MTVA-Bench: Evaluating the Language Model Inside Cascaded Voice Agentscited by 3
- 2026NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model with Tool Calling Capabilitiescited by 3
- 2026MSI-Bench: Evaluating Multi-Speaker Voice Interaction for Collaborative AI Agentscited by 3
- 2026The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMscited by 2
- 2026RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systemscited by 2
- 2026Hear2Act: Benchmarking When Prosody Should Change What an Assistant Doescited by 2
- 2026Heard but Not Heeded: Paralinguistic Information Encoding and Loss in Audio-Language Modelscited by 2
- 2026MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participantcited by 2
- 2026StepAudio 3 Realtime Technical Reportcited by 2
- 2026Qwen3.8-Omni: Towards Native Omni-Modal Agentscited by 2
- 2026ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruitycited by 1
- 2026Real-Time Voice AI Hears but Does Not Listencited by 1
- 2026Auditing Protocol-Level Shortcuts in Large Audio Language Model Judges for Speech Evaluationcited by 1
- 2026Towards More Expressive Spoken LLMs: Fine-Grained Intent Benchmarking and Acoustic-Lexical Decoupled Policy Optimizationcited by 1
- 2026Do Audio Language Models Use Paralinguistic Evidence? Counterfactual Audits for Response Evaluationcited by 1
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