Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilities
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Abstract (arXiv)
Spoken dialogue modeling poses challenges beyond text-based language modeling, requiring real-time interaction, turn-taking, and backchanneling. While most Spoken Dialogue Models (SDMs) operate in half-duplex mode-processing one turn at a time - emerging full-duplex SDMs can listen and speak simultaneously, enabling more natural conversations. However, current evaluations remain limited, focusing mainly on turn-based metrics or coarse corpus-level analyses. To address this, we introduce Full-Duplex-Bench, a benchmark that systematically evaluates key interactive behaviors: pause handling, backchanneling, turn-taking, and interruption management. Our framework uses automatic metrics for consistent, reproducible assessment and provides a fair, fast evaluation setup. By releasing our benchmark and code, we aim to advance spoken dialogue modeling and foster the development of more natural and engaging SDMs.
Key facts. Cited by 100 papers in the map (data through 14 Oct 2026). Builds on 38 of its 56 references that are in the map. First posted on arXiv 6 Mar 2025.
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38 of 56 references.
References · 38
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- 2023Textually Pretrained Speech Language Modelscited by 101
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- 2022SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilitiescited by 77
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- 2023ML-SUPERB: Multilingual Speech Universal PERformance Benchmarkcited by 71
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- 2024SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Wordscited by 68
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- 2021LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speechcited by 47
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- 2022On the Utility of Self-supervised Models for Prosody-related Taskscited by 42
- 2022IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languagescited by 40
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- 2023Paralinguistics-Enhanced Large Language Modeling of Spoken Dialoguecited by 25
- 2024Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedbackcited by 23
- 2022DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answeringcited by 15
- 2024Large Language Models Know What To Say But Not When To Speakcited by 11
- 2023GSQA: An End-to-End Model for Generative Spoken Question Answeringcited by 9
- 2024Enabling Real-Time Conversations with Minimal Training Costscited by 6
- 2024Can LLMs Understand the Implication of Emphasized Sentences in Dialogue?cited by 5
- 2025Real-Time Textless Dialogue Generationcited by 4
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Citing papers per year: 2025: 14; 2026: 86.
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- 2026IndicFDB: Benchmarking Full-Duplex Voice Agents across Indian Languages25 Sep 2026
- 2026Evaluating Real-Time Voice Agents: From Component Quality to Grounded Outcomes25 Sep 2026
- 2026Qwen-Audio-3.1-Realtime: Towards Reliable Agentic Voice Interaction21 Sep 2026
- 2026A frontend-backend architecture for tool calls in full-duplex speech models16 Sep 2026
- 2026DuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents3 Sep 2026
- 2026Agentic-DuplexGen: Decoupling Content, Timing, and Acoustics for Synthetic Dialogue Speech17 Aug 2026
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- 2025Enabling Conversational Behavior Reasoning Capabilities in Full-Duplex Speech25 Dec 2025
- 2025SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models8 Oct 2025
- 2025EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Spoken Dialogue Systems25 Aug 2025
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- 2025Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Surveycited by 39
- 2025MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Modelscited by 22
- 2025FLEXI: Benchmarking Full-duplex Human-LLM Speech Interactioncited by 15
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- 2025Game-Time: Evaluating Temporal Dynamics in Spoken Language Modelscited by 13
- 2026SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Modelscited by 13
- 2026MoshiRAG: Asynchronous Knowledge Retrieval for Full-Duplex Speech Language Modelscited by 12
- 2025FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Trainingcited by 11
- 2025From Turn-Taking to Synchronous Dialogue: A Survey of Full-Duplex Spoken Language Modelscited by 10
- 2026Multi-Faceted Interactivity Alignment in Full-Duplex Speech Modelscited by 10
12 most cited of 72 All 72 (JSON)
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- 2026DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models?13 Oct 2026
- 2026D2: High-Capability Full-Duplex Spoken Language Models from Half-Duplex Backbones12 Oct 2026
- 2026People Making Conversation: Evaluating How Naturally Speech Models Converse8 Oct 2026
- 2026DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirements30 Sep 2026
- 2026MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation29 Sep 2026
- 2026APEX-Voice: Can Voice Agents Complete Professional Workflows Through Full-Duplex Interaction28 Sep 2026
- 2026Context Spanning: A Communication Framework for Full-Duplex Speech Models and External LLM Backends27 Sep 2026
- 2026Acoustic-to-Text KV Compression for Full-Duplex Speech Models25 Sep 2026
- 2026TRACE: Temporal Audit and Condition-aware Evaluation of Streaming Video Understanding25 Sep 2026
- 2026Inquesto Score: A reliability Protocol For Voice Agents24 Sep 2026
- 2026Controlling Backchannels in Streamable Full-duplex Models24 Sep 2026
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