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arXiv · 2610.17124

D2: High-Capability Full-Duplex Spoken Language Models from Half-Duplex Backbones

Cheng-Kuang Chang·Alexander H. Liu·Brian Kingsbury·Hilde Kuehne·Rogerio Feris·Samuel Thomas·James Glass·Kai-Wei Chang
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Abstract

Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD-SLMs) in general capabilities. We propose Dialogue-to-Duplex (D2), a framework that converts pretrained HD-SLMs into high-capability FD-SLMs at fine temporal granularity. D2 adapts the original audio encoder to streaming through decoder-free representation distillation and learns full-duplex interaction through supervised fine-tuning. We systematically study the effects of chunk size on full-duplex interaction, general capabilities, and inference computation. We apply D2 to two distinct HD-SLM architectures, Qwen3-Omni and LLaMA-Omni 2. At a 12.5Hz update rate, Qwen3-Omni-D2 achieves 99.2% turn-taking success with 521-ms latency, 99.5% interruption success, and a state-of-the-art VoiceBench score of 70.1.

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Cheng-Kuang Chang, Alexander H. Liu, Brian Kingsbury, Hilde Kuehne, Rogerio Feris, Samuel Thomas, James Glass, Kai-Wei Chang. 2026-10-12. D2: High-Capability Full-Duplex Spoken Language Models from Half-Duplex Backbones. https://arxiv.org/abs/2610.17124

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