Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-world applications, such assumptions often do not hold. Visual information may be missing due to occlusions, camera failures, or privacy constraints, while multilingual speakers introduce additional complexity due to linguistic variability across languages. These challenges significantly affect the robustness and generalization of multimodal speaker identification systems. The POLY-SIM Grand Challenge 2026 aims to advance research in multimodal speaker identification under missing-modality and cross-lingual conditions. Specifically, the Grand Challenge encourages the development of robust methods that can effectively leverage incomplete multimodal inputs while maintaining strong performance across different languages. This report presents the design and organization of the POLY-SIM Grand Challenge 2026, including the dataset, task formulation, evaluation protocol, and baseline model. By providing a standardized benchmark and evaluation framework, the challenge aims to foster progress toward more robust and practical multimodal speaker identification systems.

翻译：多模态说话人识别系统通常假设在训练和测试阶段都能获得完整且同质的音视频模态。然而，在现实应用中，这一假设往往不成立。视觉信息可能因遮挡、摄像头故障或隐私限制而缺失，同时多语言说话人因不同语言间的语言变异性带来额外复杂性。这些挑战显著影响了多模态说话人识别系统的鲁棒性与泛化能力。POLY-SIM 2026大挑战赛旨在推动缺失模态与跨语言条件下多模态说话人识别的研究进展。具体而言，该挑战赛鼓励开发能够有效利用不完整多模态输入、同时在多种语言下保持高性能的鲁棒方法。本报告介绍了POLY-SIM 2026大挑战赛的设计与组织方案，包括数据集、任务定义、评估协议及基线模型。通过提供标准化基准与评估框架，本挑战赛旨在促进更鲁棒、更实用的多模态说话人识别系统的发展。