总结多语言对话语音大模型挑战赛，提供数据集与基线方法。
Summary on The Multilingual Conversational Speech Language Model Challenge: Datasets, Tasks, Baselines, and Methods
- 聚焦多语言对话语音大模型构建，设计真实场景任务
- 发布约1604小时多语言对话语音数据集，支持跨语言交互研究
- 适合语音多模态、跨语言建模方向的研究者参考
本文总结了Interspeech2025多语言对话语音语言模型（MLC-SLM）挑战赛，旨在推动高效多语言对话语音大模型（SLLMs）的构建。文中详述了挑战赛的任务设置、发布的约1604小时真实世界多语言对话语音数据集，以及参赛者可用的基线系统。该挑战赛吸引了来自13个国家的78支团队参与，提交有效排行榜结果489项，产出技术报告14份。基于参赛者的成果，本文提炼出构建多语言对话语音大模型的关键洞察，以促进该领域社区发展。
原文摘要 · Abstract (English)
This paper summarizes the Interspeech2025 Multilingual Conversational Speech Language Model (MLC-SLM) challenge, which aims to advance the exploration of building effective multilingual conversational speech LLMs (SLLMs). We provide a detailed description of the task settings for the MLC-SLM challenge, the released real-world multilingual conversational speech dataset totaling approximately 1,604 hours, and the baseline systems for participants. The MLC-SLM challenge attracts 78 teams from 13 countries to participate, with 489 valid leaderboard results and 14 technical reports for the two tasks. We distill valuable insights on building multilingual conversational SLLMs based on submissions from participants, aiming to contribute to the advancement of the community.
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