[
    {
        "id": "osp-20679",
        "type": "article-journal",
        "title": "MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation",
        "author": [
            {
                "family": "Wang",
                "given": "Ke"
            },
            {
                "family": "Ren",
                "given": "Houxing"
            },
            {
                "family": "Lu",
                "given": "Zimu"
            },
            {
                "family": "Yang",
                "given": "Yunqiao"
            },
            {
                "family": "Zong",
                "given": "Zhuofan"
            },
            {
                "family": "Zhan",
                "given": "Mingjie"
            },
            {
                "family": "Li",
                "given": "Hongsheng"
            }
        ],
        "URL": "https://omanscience.com/en/articles/multitalk-scaling-full-duplex-speech-models-to-long-multi-party-bilingual-conversation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "End-to-end full-duplex speech models have brought open-source machine conversation closer to human-like interaction, yet existing systems remain limited in two intertwined dimensions: long-context robustness and multi-party interaction. Real-world scenarios such as meetings, group lessons, and social-robot reception require a single model to track, contextualize, and respond to multiple speakers over extended durations. Progress is constrained by both data and evaluation: open multi-party speech corpora remain small and are not designed for codec-frame-level full-duplex modeling, while existing long-audio benchmarks focus on passive listening and speech-to-speech benchmarks are mostly short and dyadic. We extend the Moshi paradigm jointly along the long-horizon and multi-party axes in English and Chinese. First, we release 57.6k hours of synthetic training data ($\\href{https://huggingface.co/datasets/MultiTalk/MultiTalkPT}{MultiTalkPT}$ and $\\href{https://huggingface.co/datasets/MultiTalk/MultiTalkFT}{MultiTalkFT}$) for long-form, multi-party, English-Chinese full-duplex dialogue, with controllable length, participant count, turn-taking, overlap, backchannels, interruptions, addressee shifts, and long-range coreference. Second, we introduce $\\href{https://huggingface.co/datasets/MultiTalk/MultiTalkBench}{MultiTalkBench}$, built from real human recordings, for evaluating long-form, multi-party, bilingual full-duplex dialogue. Conversations average 32.6 minutes and include probes for long-range entity tracking, topic coherence, and addressee selection. Third, we train a bilingual Moshi-style model that sustains coherent multi-party English-Chinese conversations over extended durations and substantially outperforms open-source baselines including Moshi, MiniCPM-o-4.5, and Qwen3-Omni-30B-A3B-Instruct on MultiTalkBench."
    }
]