# Full-Duplex-Bench v1.5: Evaluating Overlap Handling for Full-Duplex Speech Models

arXiv: https://arxiv.org/abs/2507.23159 · HTML: https://mlsanity.com/paper/2507.23159/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2025.

## Built on by (most cited first, 30 of 54)

- [Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency](https://mlsanity.com/paper/2604.04847/) (2026)
- [MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models](https://mlsanity.com/paper/2511.10262/) (2025)
- [FLEXI: Benchmarking Full-duplex Human-LLM Speech Interaction](https://mlsanity.com/paper/2509.22243/) (2025)
- [GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks](https://mlsanity.com/paper/2607.05369/) (2026)
- [MoshiRAG: Asynchronous Knowledge Retrieval for Full-Duplex Speech Language Models](https://mlsanity.com/paper/2604.12928/) (2026)
- [From Turn-Taking to Synchronous Dialogue: A Survey of Full-Duplex Spoken Language Models](https://mlsanity.com/paper/2509.14515/) (2025)
- [WearVox: An Egocentric Multichannel Voice Assistant Benchmark for Wearables](https://mlsanity.com/paper/2601.02391/) (2025)
- [Raon-Speech Technical Report](https://mlsanity.com/paper/2605.23912/) (2026)
- [Instruct-FD: Can Your Full-Duplex Speech System Follow Turn-Taking Instructions?](https://mlsanity.com/paper/2607.20460/) (2026)
- [EchoChain: A Full-Duplex Benchmark for State-Update Reasoning Under Interruptions](https://mlsanity.com/paper/2604.16456/) (2026)
- [IHBench: Evaluating Post-Interruption Recovery in Voice Agents with Structured Workflows](https://mlsanity.com/paper/2606.19595/) (2026)
- [VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents](https://mlsanity.com/paper/2510.11098/) (2025)
- [Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering](https://mlsanity.com/paper/2606.11386/) (2026)
- [DuplexWorld: Can voice agents help you get through the day?](https://mlsanity.com/paper/2608.10716/) (2026)
- [Realtime-Venus: A full-duplex interaction system with asynchronous delegation](https://mlsanity.com/paper/2609.13814/) (2026)
- [NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model with Tool Calling Capabilities](https://mlsanity.com/paper/2609.21967/) (2026)
- [MSI-Bench: Evaluating Multi-Speaker Voice Interaction for Collaborative AI Agents](https://mlsanity.com/paper/2609.24812/) (2026)
- [F-Actor: Controllable Conversational Behaviour in Full-Duplex Models](https://mlsanity.com/paper/2601.11329/) (2026)
- [ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models](https://mlsanity.com/paper/2604.10065/) (2026)
- [M3-DuplexBench: A Multi-Turn, Multilingual, Multidomain Benchmark for Full-Duplex Spoken Dialogue Models](https://mlsanity.com/paper/2607.29125/) (2026)
- [X2-Turn: Frame-Synchronous Dual-Head Modeling for Joint Streaming ASR and Turn State Prediction](https://mlsanity.com/paper/2608.10878/) (2026)
- [VoiceChat-TTS: A Low-Latency Continuous Speech Synthesis Model for Interactive Agents](https://mlsanity.com/paper/2608.13831/) (2026)
- [EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Spoken Dialogue Systems](https://mlsanity.com/paper/2508.17623/) (2025)
- [Adaptive Turn-Taking for Real-time Multi-Party Voice Agents](https://mlsanity.com/paper/2606.13544/) (2026)
- [Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO](https://mlsanity.com/paper/2607.27756/) (2026)
- [MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant](https://mlsanity.com/paper/2609.13076/) (2026)
- [Same Words, Different Actions: Paired Turn-Taking Evaluation under Rewritten Dialogue Contexts](https://mlsanity.com/paper/2609.17360/) (2026)
- [Multilingual and Continuous Backchannel Prediction: A Cross-lingual Study](https://mlsanity.com/paper/2512.14085/) (2025)
- [Enabling Conversational Behavior Reasoning Capabilities in Full-Duplex Speech](https://mlsanity.com/paper/2512.21706/) (2025)
- [Unit-Based Agent for Semi-Cascaded Full-Duplex Dialogue Systems](https://mlsanity.com/paper/2601.20230/) (2026)

## Abstract (arXiv)

Full-duplex spoken dialogue systems promise to transform human-machine interaction from a rigid, turn-based protocol into a fluid, natural conversation. However, the central challenge to realizing this vision, managing overlapping speech, remains critically under-evaluated. We introduce Full-Duplex-Bench v1.5, the first fully automated benchmark designed to systematically probe how models behave during speech overlap. The benchmark simulates four representative overlap scenarios: user interruption, user backchannel, talking to others, and background speech. Our framework, compatible with open-source and commercial API-based models, provides a comprehensive suite of metrics analyzing categorical dialogue behaviors, stop and response latency, and prosodic adaptation. Benchmarking five state-of-the-art agents reveals two divergent strategies: a responsive approach prioritizing rapid response to user input, and a floor-holding approach that preserves conversational flow by filtering overlapping events. Our open-source framework enables practitioners to accelerate the development of robust full-duplex systems by providing the tools for reproducible evaluation.