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English(EN) SEAR: Segment-Evidence-Aware Routing for Weak-to-Strong Multilingual Speech MCQ

SEAR系统在多语言语音挑战赛中准确率达90.92%

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为多语言对话语音语言模型（MLC-SLM）挑战赛开发了一个名为SEAR的系统，准确率达到90.92%。该系统通过将ASR转录转换为事件跨度来调整Qwen3-Omni-30B-A3B-Instruct模型，然后利用这些跨度来合成语义和声学选择题。这些选择题使用Qwen3.6-27B生成语义选择题，使用Gemini 3.1 Flash-Lite生成声学选择题，并经过严格的验证步骤确保数据质量。 AI

影响 这项研究推进了多语言语音理解和语言模型数据合成的技术。

排序理由 该项目是一篇研究论文，详细介绍了一个用于特定挑战的系统。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

SEAR系统在多语言语音挑战赛中准确率达90.92%

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该项目是一篇研究论文，详细介绍了一个用于特定挑战的系统。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Huy Hoang Le, Long-Bao Nguyen, Minh Tri Dao ·

    SEAR：面向弱到强多语言语音MCQ的分割-证据感知路由

    arXiv:2609.11355v1 Announce Type: new Abstract: This paper describes our system for Task~2 of the second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge. We adapt Qwen3-Omni-30B-A3B-Instruct with a segment-evidence-aware data and post-training pipeline. A la…

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