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
arXiv AI
Jun 9

Liberating LLM Capabilities in Full-Duplex Speech Models

arXiv:2606. 07547v1 Announce Type: cross Abstract: Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs.

By Luoyuan Zhang, Bokai Xu, Junbo Cui, Weiyue Sun, Yingjing Xu, Hanyu Liu, Yuan Yao
arXiv Computation and Language
Oct 9

DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion

DiffuPlex is a rolling masked diffusion framework designed to accelerate full‑duplex spoken dialog models by predicting multiple future user and assistant frames in a single backbone pass. It consumes only a confident prefix of each predicted future while maintaining the original frame rate, and revises unplayed future content when the interaction diverges. Two inference policies—DiffuPlex‑LISTEN and DiffuPlex‑SPEAK—offer speedups of 1.46×–1.59× in deployment‑path wall‑clock time and 1.61×–1.80× in Core LM speed, with all backbone invocations completing within the 80 ms interaction interval, while preserving conversational quality and, in the LISTEN policy, speech naturalness.

By Heeseung Kim