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New frameworks advance full-duplex spoken language models for real-time interaction · 3 sources tracked

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Researchers have developed new frameworks and benchmarks for full-duplex spoken language models (FD-SLMs), which allow for simultaneous listening and speaking. DuplexAct introduces a benchmark with a three-level action taxonomy and real-world conversational data to evaluate interaction actions in FD-SLMs. The D2 framework converts existing half-duplex models into high-capability FD-SLMs with low latency, achieving state-of-the-art performance on benchmarks like VoiceBench. Additionally, JAL-Turn offers a lightweight, speech-only approach for real-time turn-taking detection in dialogue systems by jointly modeling acoustic and linguistic cues. AI

IMPACT These advancements in full-duplex spoken language models could lead to more natural and responsive voice AI agents and virtual assistants.

RANK_REASON The cluster consists of three arXiv papers introducing new benchmarks and frameworks for spoken language models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New frameworks advance full-duplex spoken language models for real-time interaction · 3 sources tracked

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The cluster consists of three arXiv papers introducing new benchmarks and frameworks for spoken language models.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Jiaheng Dong, Shihong Tan, Gongping Huang, Jean Honorio, Abhirup Ghosh, Ting Dang ·

    DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models?

    arXiv:2610.17738v1 Announce Type: new Abstract: End-to-end (E2E) full-duplex spoken language models (FD-SLMs) enable simutanous listening and speaking continously, requiring models to take appropriate interaction actions as conversations unfold. However, existing benchmarks lack …

  2. arXiv cs.AI TIER_1 English(EN) · Cheng-Kuang Chang, Alexander H. Liu, Brian Kingsbury, Hilde Kuehne, Rogerio Feris, Samuel Thomas, James Glass, Kai-Wei Chang ·

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

    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-SLMs) in general capabilities. We propose Dialogue-to-Duplex (D2), a framework t…

  3. arXiv cs.AI TIER_1 English(EN) · Yu Pan, Guangzhao Yang, Muhammad Huzaifah, Ningjie Bai, Shi Qiu ·

    JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems

    arXiv:2603.26515v2 Announce Type: replace-cross Abstract: Efficient and robust turn-taking detection remains a significant challenge in industrial-grade voice AI agents. Existing systems often rely on acoustic or linguistic cues alone, limiting accuracy and stability, whereas dir…

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