arXiv Computation and Language
Sep 18

Full-Duplex Speech Models Take the Floor When Asked, Not When Needed

Full‑duplex speech models can listen and speak simultaneously, but they struggle to decide when to speak. Experiments with five model families show that being addressed or encountering silence are reliable triggers, whereas cues like false facts or hazards are not. Even when models answer questions, they rarely challenge false claims or warn about danger, revealing a gap in content understanding and intervention decisions.

By Linkai Peng, Baorian Nuchged, Kaiqi Fu, Yuyang Yao
arXiv Computation and Language
Oct 12

People Making Conversation: Evaluating How Naturally Speech Models Converse

The study evaluates conversational speech models by placing them at 427 pauses and 482 turn ends from 176 unscripted conversations, scoring their responses on timing, latency, length, wording, and recall of recent dialogue. Hosted APIs (GPT Realtime, Gemini 3.1 Flash Live, Grok Voice) respond at almost every pause, with latency ranging from 1–4 s for silence-based endpointers and about 8 s for semantic turn detection, while full‑duplex models (PersonaPlex, Moshi) remain silent more often and can interrupt within human reaction time. Recall rates vary: hosted systems remember 41–60 % of facts under a Claude Opus 5.5 judge and 19–26 % with a strict word matcher; full‑duplex models recall 17–22 % and 12–15 % respectively.

By Arya Bastani, Iman Modarressi