The paper introduces a frontend‑backend architecture for full‑duplex speech‑to‑speech models that enables tool calls while preserving natural conversational flow. The frontend emits a delegation token and streams ASR transcripts to a text‑based backend LLM, which performs tool calls and returns results that are re‑injected into the frontend via a lightweight prefill‑and‑repeat mechanism before streaming TTS synthesis. In single‑turn evaluations the system achieves 92‑97% tool‑call recall, 81.2% accuracy in rejecting irrelevant calls, and competitive performance on Full‑Duplex‑Bench‑V3 and EVA‑Bench when paired with a large backend model.
arXiv:2609.21967v1 Announce Type: cross Abstract: We introduce NemotronLabs VoiceChat, an open full-duplex speech-to-speech model with native tool-calling capabilities. NemotronLabs VoiceChat combine...
The paper introduces a lightweight ASR head that can be added to full‑duplex speech‑to‑speech models, enabling real‑time user transcription without major architectural changes. The method adds only a few parameters and preserves full‑duplex conversational features such as turn‑taking and barge‑in. Experiments show a streaming WER of 10.21% within the duplex framework and 7.73% when trained as a standalone ASR model, matching state‑of‑the‑art performance.
arXiv:2610.34653v1 Announce Type: new Abstract: Latency-sensitive spoken agents must know not only which tool to call, but also when they have heard enough to act. Waiting for the turn to complete de...
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.
arXiv:2609.38867v1 Announce Type: new Abstract: Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular in...
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:2610.17124v1 Announce Type: cross Abstract: Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD...
The paper introduces SimIF-Bench, a benchmark that tests whether full‑duplex conversational models can stay within a prescribed scenario and complete multiple goals in order. It shows that current open‑source models struggle with these constraints. The authors then propose a GDPO‑based training recipe that allows a full‑duplex model, named SteerablePlex, to follow textual instructions during conversation while preserving turn‑taking, and demonstrate that it outperforms existing open‑source models and GPT‑Realtime when used as a controllable user simulator.
arXiv:2609.13814v1 Announce Type: new Abstract: Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and li...
Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and linguistic cues, while video interaction also requi...
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.
Apriel Voice 27B Preview is an open speech‑language model that unifies speech recognition, contextual reasoning, conversation maintenance, and tool use within a single reasoning backbone. Built on the post‑trained Gemma 4 26B‑A4B checkpoint, it processes speech directly and generates responses and tool calls without a separate dialogue model, using a two‑stage training procedure that adapts the projector and backbone through supervised fine‑tuning, text replay, and cross‑entropy/KL supervision. The model achieves strong performance on benchmarks such as Big Bench Audio (98.90), Speech‑BFCL (81.13), and τ‑Voice (65.51% task success), with a mean latency of 1.91 s, and is deployed on a custom serving platform for interactive voice interactions.