Learning Speaker Identity Beyond Language and Modality Constraints: Insights from the POLY-SIM 2026 Challenge

2026-07-15

AI Summary

This work addresses the limited robustness and generalization of multimodal speaker recognition under modality-missing and cross-lingual conditions. To overcome the reliance of conventional approaches on complete modalities and monolingual assumptions, the study establishes the first systematic evaluation framework that integrates multimodal fusion, cross-lingual representation learning, and modality robustness modeling to effectively handle heterogeneous and incomplete inputs in non-ideal scenarios. The project delivers a reproducible benchmark platform and, for the first time in a unified challenge, incorporates settings with missing modalities and multilingual speakers. This setup reveals critical performance bottlenecks of current methods in realistic, complex environments and provides clear directions for future improvements.

Abstract

Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing, and assume each speaker only speaks a single language. However, in real-world applications, such assumptions often do not hold. Visual or audio information may be missing due to occlusions, camera or microphone failures, or privacy constraints. Multilingual speakers introduce additional complexity due to linguistic variability across languages. These situations constitute substantial challenges for the robustness and generalization capabilities of multimodal speaker identification systems. Aim of the POLY-SIM 2026 challenge is to address these aspects of speaker identification and to provide a standardized setup for the comparison of the proposed solutions.

Problem

• multimodal speaker identification
• missing modalities
• multilingual speakers
• robustness
• generalization

Innovation

• multimodal speaker identification
• modality missing
• multilingual speakers
• robustness
• generalization