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
arXiv:2607. 01345v1 Announce Type: cross Abstract: Turn-taking naturalness is central to full-duplex spoken dialogue systems, yet its automatic evaluation remains limited.
By Hao Zhang, Thomas Thebaud, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Velazquez
arXiv:2606. 13544v1 Announce Type: cross Abstract: Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations.
By Soumyajit Mitra, Prabhat Pandey, Abhinav Jain, Shanmukha Sahith, K V Vijay Girish
arXiv:2607.26178v2 Announce Type: replace
Abstract: Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current mo...
By Takyoung Kim, Kang-wook Kim, Sang Hoon Woo, Julia Hirschberg, Gunhee Kim, Dilek Hakkani-T\"ur
Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings.
arXiv:2607. 15648v1 Announce Type: cross Abstract: In multi-party dialogues between a dialogue system and multiple users, identifying to whom an utterance is addressed is a key challenge.
By Taiga Mori, Koji Inoue, Divesh Lala, Tatsuya Kawahara
arXiv:2608.22071v1 Announce Type: cross
Abstract: Turn-taking is a basic organizational feature of human conversation and remains difficult to model in natural, synchronous dialog systems. While exis...
By Ahmet Tu\u{g}rul Bayrak, Fatma Nur Korkmaz, Bekir Berker T\"urker, Mustafa Serta\c{c} T\"urkel, Alper Kaplan
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
The paper introduces a decoupled data approach for the Neural Finite State Machine (NFSM) framework to improve full‑duplex dialogue. It serializes real human‑human spoken dialogues into FSM tapes using a rule‑based event‑guided transformation, while shaping semantics through human‑agent text dialogues. A Source‑Aware Calibrated (SAC) loss is proposed to balance state‑transition token distribution and align each data source with its strongest supervisory signal, leading to better turn‑taking performance without sacrificing semantic quality.
By Yihang Li, Chenhui Chu
arXiv:2607. 22635v1 Announce Type: new Abstract: Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations.
By Xuzhao Geng, Haozhao Wang, Xuelian Li, Zhenyu Yang, Haonan Lu, Rui Zhang, Ruixuan Li
The paper proposes using semantic uncertainty, derived from large language models, to predict Transition Relevance Places (TRPs) in spoken dialogue. By sampling possible continuations of an ongoing turn and measuring changes in semantic dispersion, the authors identify moments when a listener might take the floor. Their method outperforms prompt-based and fine-tuned text-only baselines on a dataset with real-time TRP labels, supporting the idea that evolving semantic constraints inform turn‑taking opportunities in unscripted interaction.
By Muhammad Umair, Jan P. de Ruiter
The paper introduces a method for forecasting conversational derailment by incorporating speech act information as an auxiliary signal to enhance pragmatic representations. This approach aims to reduce lexical noise and improve generalizability, especially in low-data and cross-domain scenarios. Experiments on three datasets demonstrate performance gains over existing methods.
By Angela Yifei Yuan, Christine De Kock, Christopher Leckie