# FD-Bench: A Full-Duplex Benchmarking Pipeline Designed for Full Duplex Spoken Dialogue Systems

arXiv: https://arxiv.org/abs/2507.19040 · HTML: https://mlsanity.com/paper/2507.19040/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities](https://mlsanity.com/paper/2305.11000/) (2023)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://mlsanity.com/paper/2412.10117/) (2024)
- [AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head](https://mlsanity.com/paper/2304.12995/) (2023)
- [GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot](https://mlsanity.com/paper/2412.02612/) (2024)
- [Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming](https://mlsanity.com/paper/2408.16725/) (2024)
- [VoiceBench: Benchmarking LLM-Based Voice Assistants](https://mlsanity.com/paper/2410.17196/) (2024)
- [VITA: Towards Open-Source Interactive Omni Multimodal LLM](https://mlsanity.com/paper/2408.05211/) (2024)
- [Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM](https://mlsanity.com/paper/2411.00774/) (2024)
- [MinMo: A Multimodal Large Language Model for Seamless Voice Interaction](https://mlsanity.com/paper/2501.06282/) (2025)
- [Language Model Can Listen While Speaking](https://mlsanity.com/paper/2408.02622/) (2024)
- [OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation](https://mlsanity.com/paper/2410.17799/) (2024)
- [SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training](https://mlsanity.com/paper/2412.15649/) (2024)
- [A Full-duplex Speech Dialogue Scheme Based On Large Language Models](https://mlsanity.com/paper/2405.19487/) (2024)
- [Building a Taiwanese Mandarin Spoken Language Model: A First Attempt](https://mlsanity.com/paper/2411.07111/) (2024)
- [Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model](https://mlsanity.com/paper/2402.10642/) (2024)

## Built on by (most cited first, 20 of 20)

- [Game-Time: Evaluating Temporal Dynamics in Spoken Language Models](https://mlsanity.com/paper/2509.26388/) (2025)
- [From Turn-Taking to Synchronous Dialogue: A Survey of Full-Duplex Spoken Language Models](https://mlsanity.com/paper/2509.14515/) (2025)
- [EchoChain: A Full-Duplex Benchmark for State-Update Reasoning Under Interruptions](https://mlsanity.com/paper/2604.16456/) (2026)
- [Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs](https://mlsanity.com/paper/2607.06540/) (2026)
- [Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering](https://mlsanity.com/paper/2606.11386/) (2026)
- [Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models](https://mlsanity.com/paper/2603.16874/) (2026)
- [M3-DuplexBench: A Multi-Turn, Multilingual, Multidomain Benchmark for Full-Duplex Spoken Dialogue Models](https://mlsanity.com/paper/2607.29125/) (2026)
- [Synchronization and Turn-Taking in Full-Duplex Speech Dialogue Models](https://mlsanity.com/paper/2605.20356/) (2026)
- [VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents](https://mlsanity.com/paper/2605.30256/) (2026)
- [Agentic-DuplexGen: Decoupling Content, Timing, and Acoustics for Synthetic Dialogue Speech](https://mlsanity.com/paper/2608.16053/) (2026)
- [DuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents](https://mlsanity.com/paper/2609.03423/) (2026)
- [Full-Duplex Speech Models Take the Floor When Asked, Not When Needed](https://mlsanity.com/paper/2609.19596/) (2026)
- [MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation](https://mlsanity.com/paper/2609.36903/) (2026)
- [Fast When, Careful Who: Dual-Process Multiparty Turn-Taking with Diffusion Augmentation](https://mlsanity.com/paper/2606.16568/) (2026)
- [Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents](https://mlsanity.com/paper/2609.13117/) (2026)
- [Neither Silence nor Overlap Is Failure: Intent-Conditioned Evaluation of Turn-Taking in Full-Duplex Spoken Dialogue Models](https://mlsanity.com/paper/2609.27372/) (2026)
- [APEX-Voice: Can Voice Agents Complete Professional Workflows Through Full-Duplex Interaction](https://mlsanity.com/paper/2609.34973/) (2026)
- [DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirements](https://mlsanity.com/paper/2609.39446/) (2026)
- [Conversation Is a Two-Body Problem: Dyadic Evaluation of Full-Duplex Dialogue Models](https://mlsanity.com/paper/2610.08125/) (2026)
- [DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models?](https://mlsanity.com/paper/2610.17738/) (2026)

## Abstract (arXiv)

Full-duplex spoken dialogue systems (FDSDS) enable more natural human-machine interactions by allowing real-time user interruptions and backchanneling, compared to traditional SDS that rely on turn-taking. However, existing benchmarks lack metrics for FD scenes, e.g., evaluating model performance during user interruptions. In this paper, we present a comprehensive FD benchmarking pipeline utilizing LLMs, TTS, and ASR to address this gap. It assesses FDSDS's ability to handle user interruptions, manage delays, and maintain robustness in challenging scenarios with diverse novel metrics. We applied our benchmark to three open-source FDSDS (Moshi, Freeze-omni, and VITA-1.5) using over 40 hours of generated speech, with 293 simulated conversations and 1,200 interruptions. The results show that all models continue to face challenges, such as failing to respond to user interruptions, under frequent disruptions and noisy conditions. Demonstrations, data, and code will be released.