arXiv Machine Learning
Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Computation and Language
Aug 27
TurnBench: A Multi-Domain Benchmark for Turn-Taking Dynamics in Spoken Dialogue
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.
arXiv AI
Jul 3
TurnNat: Automatic Evaluation of Turn-Taking Naturalness in Dyadic Spoken Dialogue
arXiv:2607. 01345v1 Announce Type: cross Abstract: Turn-taking naturalness is central to full-duplex spoken dialogue systems, yet its automatic evaluation remains limited.
arXiv AI
Jun 12
Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
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.
arXiv Computation and Language
Sep 1
DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues
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...
Hugging Face Trending Papers
Jun 11
Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
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 AI
Jul 20
On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels
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.