Scalable Context Orchestration for Serving LLMs Over Voice presents llmovoice, a middleware that explicitly models voice context—including speaking rate, background noise, and packet loss—to guide large language model responses. By constructing a bounded voice context at each turn, llmovoice improves alignment with user preferences and reduces errors, achieving a 52.4% drop in speaking‑rate alignment error and a 0.9% false‑interruption rate under packet loss. In addition, it cuts model usage costs dramatically, lowering per‑turn cost by up to 24.9× while maintaining 98.7% of baseline answer quality in long sessions.
arXiv:2608. 07631v1 Announce Type: cross Abstract: LLM-based full-duplex voice services allow users to speak while the assistant is responding.
By Shibo Wang, Zicheng Zhang, Libo Wang, Junfeng Ma
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:2603.16783v2 Announce Type: replace
Abstract: Robust voice agents require exposure to the full diversity of how people interact through speech. However, obtaining enough spoken interactions is...
By Jonggeun Lee, Junseong Pyo, Jeongmin Park, Yohan Jo
RelayS2S is a hybrid real‑time dialogue system that runs a fast duplex speech‑to‑speech path and a slow ASR‑to‑LLM path in parallel. The fast path speculatively drafts a short response prefix and streams it to TTS, while the slow path generates a higher‑quality continuation conditioned on that prefix. A lightweight verifier decides whether to commit the prefix or fall back to the cascaded pipeline, achieving much lower latency (81 ms P90 first‑chunk) while preserving 99% of the cascaded pipeline’s textual quality.
By Long Mai, Junli Liang
arXiv:2605.13841v3 Announce Type: replace-cross
Abstract: Voice agents are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses realistic conversatio...
By Tara Bogavelli, Gabrielle Gauthier Melan\c{c}on, Katrina Stankiewicz, Oluwanifemi Bamgbose, Fanny Riols, Hoang H. Nguyen, Raghav Mehndiratta, Lindsay Devon Brin, Joseph Marinier, Hari Subramani, Anil Madamala, Sridhar Krishna Nemala, Srinivas Sunkara