Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents
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Abstract (arXiv)
Full-duplex evaluation often emphasizes whether an agent keeps speaking or stops. That binary cannot express a third response humans use routinely: continuing to speak while incorporating what the listener just contributed. The contribution may be a missing word, a correction or a clarification. We introduce Duplex Cue, an evaluation of this in-turn adaptation in full-duplex voice agents. Duplex Cue separates listener intent (backchannel, collaboration, or interruption) from speaker behavior: continuing unchanged, adapting within the turn, or yielding. Adaptation includes acknowledgment as well as content revision. In a single-model case study using 300 human-confirmed cues from unscripted English conversations, we compare recorded human responses with PersonaPlex continuations generated while replaying the listener's audio. We retain 208 pairs with the ongoing speaker active at cue onset and a scorable response in each condition. On the 66 collaborative pairs, recorded speakers adapt in 68.2% of cases, compared with 34.8% for PersonaPlex. The model otherwise continues unchanged (42.4%) or yields (22.7%). These findings show why evaluating natural voice interaction requires measuring how an agent responds to a listener's contribution as well as whether it keeps speaking.
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References · 19
- 2024Moshi: a speech-text foundation model for real-time dialoguecited by 568
- 2022Generative Spoken Dialogue Language Modelingcited by 126
- 2025Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilitiescited by 100
- 2024Beyond Turn-Based Interfaces: Synchronous LLMs as Full-Duplex Dialogue Agentscited by 72
- 2026PersonaPlex: Voice and Role Control for Full Duplex Conversational Speech Modelscited by 63
- 2025Full-Duplex-Bench v1.5: Evaluating Overlap Handling for Full-Duplex Speech Modelscited by 54
- 2025Talking Turns: Benchmarking Audio Foundation Models on Turn-Taking Dynamicscited by 47
- 2025Full-Duplex-Bench-v2: A Multi-Turn Evaluation Framework for Duplex Dialogue Systems with an Automated Examinercited by 41
- 2026Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluencycited by 37
- 2025MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Modelscited by 22
- 2025NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Predictioncited by 20
- 2025FD-Bench: A Full-Duplex Benchmarking Pipeline Designed for Full Duplex Spoken Dialogue Systemscited by 20
7 more
- 2025FLEXI: Benchmarking Full-duplex Human-LLM Speech Interactioncited by 15
- 2026Full-Duplex Interaction in Spoken Dialogue Systems: A Comprehensive Study from the ICASSP 2026 HumDial Challengecited by 13
- 2026A Survey of Full-Duplex Spoken Dialogue Systems: Architectural Hierarchy, Interaction Ontology, and Decision State Machinecited by 8
- 2026TurnBench: A Multi-Domain Benchmark for Turn-Taking Dynamics in Spoken Dialoguecited by 7
- 2026EchoChain: A Full-Duplex Benchmark for State-Update Reasoning Under Interruptionscited by 5
- 2026Semantic-Aware Interruption Detection in Spoken Dialogue Systems: Benchmark, Metric, and Modelcited by 4
- 2025TurnGuide: Enhancing Meaningful Full Duplex Spoken Interactions via Dynamic Turn-Level Text-Speech Interleavingcited by 1
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