POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan

TL;DR

The POLY-SIM Grand Challenge 2026 seeks to develop and evaluate robust multimodal speaker identification methods capable of handling missing modalities and cross-lingual variability, with a standardized benchmark.

Contribution

It introduces a comprehensive dataset, task formulation, evaluation protocol, and baseline model for the challenge, fostering progress in robust multimodal speaker identification.

Findings

Benchmark dataset and evaluation framework established.

Baseline models provided for comparison.

Focus on robustness to missing modalities and multilingual conditions.

Abstract

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.