M3-DuplexBench is a new benchmark designed to evaluate full‑duplex spoken dialogue systems (FDSDSs) that can listen while speaking. It covers multi‑turn interactions in both English and Japanese across casual conversation and question‑answering domains, jointly assessing interaction timing and response content. The benchmark also offers a reference‑conditioned setting to test sensitivity to dialogue history and trajectory mismatch.
By Ryo Fukuda, Atsushi Ando, Hiroki Kanagawa, Takatomo Kano, Marc Delcroix, Naohiro Tawara, Yuya Chiba
arXiv:2609.39446v1 Announce Type: new
Abstract: Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions. We introduce D...
By Keyue Xing, Wentao Ding, Mengmeng Wang, Wenming Tu, Zilong Zheng, Yipeng Kang
arXiv:2608.28630v1 Announce Type: cross
Abstract: Compared with half-duplex dialogue systems where the system waits for user turn completion before it responds, natural full-duplex dialogue systems r...
By Tianrui Pan, Qinglin Zhang, Chong Deng, Luyao Cheng, Qian Chen, Wen Wang, Jie Tang, Gangshan Wu, Jie Liu
TurnBench is a new multi‑domain benchmark for evaluating turn‑taking dynamics in spoken dialogue. It comprises a 30‑hour hand‑labeled corpus of dyadic human conversations, a standardized evaluation protocol for end‑of‑turn and interruption detection, and covers six distinct interaction styles with triple annotation. The benchmark also provides a 104‑hour training set, a public leaderboard, and an interactive dataset viewer at https://turnbench.sesame.com.
By Freeman Jiang, Ramon Sanabria, Soham Deshmukh, Bandhav Veluri, Simon Michael Vuch Williams, Elliott K. Suen, Garreth Lee, Kevin Yoonho Choi, Takuya Umeki, Riku Kubo, Sathvik Udupa, Chien-yu Huang, Shih-Yun Shan Kuan, Zhuoyan Tao, Satyapriya Krishna, Sefik Emre Eskimez, Yu Tsao, Hung-yi Lee, Shinji Watanabe
SteerDuplex is a full‑duplex speech dialogue model that can be steered along attributes such as tone, persona, speaking rate, and voice style in response to user instructions. The authors introduce a taxonomy of text‑ and audio‑based steerability, identify gaps in existing models, and fine‑tune a Moshi‑based model with reinforcement learning to improve timing and response continuity. They also present SteerBench, a benchmark of 390 spoken prompts and 1,067 human‑authored rubrics, showing significant gains in audio‑steering pass rates and interruption handling compared to open baselines.
By Utkarsh Tyagi, Ramaneswaran Selvakumar, Advait Gosai, Sonal Kumar, Nikhil Barhate, Isabell Sagar, Steven Li, Miheer Bavare, Daniel Quigley, Fabiola Tapia Carrillo, Jose M Patron E, Diego Mac\'ias Guti\'errez, Paul Song, Ramani Duraiswami, Dinesh Manocha, Yunzhong He
arXiv:2606.11167v2 Announce Type: replace
Abstract: Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, cur...
By Atsumoto Ohashi, Neil Zeghidour, Alexandre D\'efossez, Eugene Kharitonov
arXiv:2607. 20460v1 Announce Type: cross Abstract: Current full-duplex (FD) spoken dialogue systems can produce fluid interactions, yet it remains unclear whether they can adapt their turn-taking behavior when explicitly instructed.
By Yuzhi Tang, Wentao Ma, Xiling Zhao, Ahmad Salimi, Sepehr Harfi Moridani, Dongming Shen, Jixuan Wang, Abdulrahman Abdulrazzag, Murdock Aubry, Yu-Hua Chen, Daniel Lee, Jaewon Lee, Jonah Mackey, Silin Meng, Nicholas Stranges, Chenxu Xiong, Hao Yu, Yi Zhu, Mu Li, Alex Smola
The paper introduces a pipeline that generates intent‑labeled, two‑channel conversational speech from relational event lists, enabling controlled synthesis of full‑duplex dialogue with 42 phenomena across eight families in English and Mandarin. By having an LLM author each event’s speaker, text, conversational act, and attachment, and then aligning and timing these events independently, the system produces diverse, realistic turn‑taking signals. Experiments show that models trained on this synthetic corpus achieve higher floor‑occupancy accuracy and better start‑speaking/listening F1 scores compared to models trained on prior data.
By Matthew Sun, Vinay Kothapally, Meng Yu, Chao Huang, Hao Zhang, Yixuan Zhang, Steve Yves
The paper introduces ELLSA 2, an end‑to‑end full‑duplex dialogue model that jointly optimizes speech interaction and streaming gesture‑expression generation. It uses an autoregressive, chunk‑wise synthesizer that combines pretrained acoustic features with distinct listening and speaking hidden states, enabling continuous non‑verbal generation even when the model is silent. The authors also present contextual semantic calls and template guidance to separate the decision to invoke a semantic gesture from its realization, and they establish a curated data and evaluation pipeline for dialog‑aware co‑speech gesture generation, showing that ELLSA 2 outperforms cascaded baselines and that semantic template guidance significantly improves gesture quality.
By Siyin Wang, Wenyi Yu, Terumi Chiba, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Chao Zhang
ECHO is a new paired diagnostic benchmark designed for Chinese full‑duplex spoken dialogue systems to evaluate context‑sensitive turn‑taking. It pairs examples that share the same overlap transcript but differ in preceding multi‑turn context, requiring either a Yield or Keep decision, and also includes off‑talk cases to test unnecessary yielding. The benchmark introduces pair accuracy, penalizing constant‑action policies, and shows that many systems are biased toward Yield, performing better on interruptions than on backchannels.
By Shuofeng Zhao, Hongwei Cai, Wenke Fan, Qingxiang Guo, Dawei Yang, Zhou Wang, Zhiyang Zhou, Yingxin Shang, Weixu Wang, Lin Yang, Shuran Zhou, Yang Song
DuplexDrama is a newly announced synthesized spoken dialogue dataset that uniquely combines complete persona and scenario settings, three full‑duplex behaviors (interruption, backchannel, incomplete), expressive speech with persona‑aligned emotion labels, and script‑aware sound events. The dataset was created through a four‑stage pipeline and validated for quality on both scripts and audio, yielding over 2,000 hours of audio featuring 64 voices across 13 personas and 5 age groups, with 3.8% of turns containing full‑duplex behaviors. A curated bilingual subset of 6,400 dialogues (800 hours total) will be released to support research in full‑duplex spoken dialogue models, and evaluation prompts will accompany the dataset.
By Qingxiang Guo, Wenke Fan, Shuofeng Zhao, Dawei Yang, Zhiyang Zhou, Yingxin Shang, Hongwei Cai, Zhou Wang, Weixu Wang, Lin Yang, Shuran Zhou, Yang Song
arXiv:2507.18061v4 Announce Type: replace-cross
Abstract: Spoken Language Models (SLMs) are expected to support natural spoken interaction beyond task completion. However, existing SLM benchmarks pri...
By Zehan Li, Hongjie Chen, Qing Wang, Yuxin Zhang, Jing Zhou, Hang Lv, Mengjie Du, Yaodong Song, Jie Lian, Jian Kang, Jie Li, Yongxiang Li