arXiv Computation and Language
Beyond Two Tracks: Speaker-Anchored Full-Duplex Dialogue Recovery
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
Hugging Face Trending Papers
Sep 8
ConversationalVoice: Full-Duplex Speech Data from Real Conversations through Source-Faithful Reconstruction and Conversation-Grounded Expansion
Full-duplex speech models require training data that preserves turn-taking, overlap, interruption, and backchannel behavior, yet these signals are entangled across speakers in noisy real-world recordi...
arXiv Computation and Language
Sep 21
Voice-Light: A Full-Duplex Cascaded Voice Agent with Causal Turn-Taking and Speculative Generation
arXiv:2609.20995v1 Announce Type: cross Abstract: Natural spoken interaction requires more than streaming ASR, language generation, and speech synthesis: a system must react to overlap without cancel...
arXiv AI
Oct 16
Reference-Guided Turn-Taking Style Transfer for Full-Duplex Spoken Language Models
The paper introduces a method for controlling the turn‑taking behavior of full‑duplex spoken language models by identifying a single late‑layer residual‑stream direction, called the initiative vector, that determines how much the model speaks. By extracting this vector from the model’s own conversations without additional training, the authors can anticipate speech onsets up to 800 ms ahead and adjust the speech ratio from 6–12 % to 42–61 % through scaling during generation. A short reference recording of a target speaker allows the model to adopt that speaker’s turn‑taking style, and the transfer is highly speaker‑specific, with mismatched references degrading performance in 93–98 % of cases.
arXiv AI
Sep 30
MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation
arXiv:2609.36903v1 Announce Type: cross Abstract: End-to-end full-duplex speech models have brought open-source machine conversation closer to human-like interaction, yet existing systems remain limi...
arXiv Computation and Language
Oct 15
MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue
arXiv:2610.20172v1 Announce Type: cross Abstract: Full-duplex speech systems must decide when to enter, hold, or relinquish the conversational floor, even as multiple speakers overlap or backchannel...
arXiv AI
Oct 7
PERSIST: Who-What-When Memory Across Sessions for Full-Duplex Spoken Dialogue
PERSIST is a persistent memory system designed for multi-session, multi-speaker spoken dialogue, explicitly modeling Who, What, and When to enable voice assistants to recall past conversations and adapt to individual users. It structures cross-session histories into readable event records and retrieves them using a 3W joint scoring mechanism that combines semantic content, acoustic speaker identity, and temporal state. By reusing intermediate representations from the dialogue backbone, PERSIST reduces retrieval latency from 578.42 ms to 7.03 ms and achieves 85.08% end‑to‑end task accuracy on the SpokenTrace benchmark, improving EM@3 from 49.01% to 82.10%.