arXiv:2606.29335v1 Announce Type: new Abstract: Multimodal speaker identification systems face two key challenges in real-world deployment: missing modalities and language mismatch between training and testing conditions. In practical scenarios, background multi-speaker conversations, ambient noise, and overlapping speech further degrade identification accuracy. To address these challenges, we propose a multimodal polyglot speaker identification system for the POLY-SIM 2026 Grand Challenge. The system is fundamentally built upon Adaptive Modality Routing(AMR), a modality fusion module that dyn
The proliferation of multimodal AI applications and the increasing complexity of real-world audio environments necessitate robust solutions for speaker identification.
This research addresses fundamental challenges in reliable speaker identification, which is crucial for the deployment of advanced AI agents and secure biometric systems.
The proposed Adaptive Modality Routing (AMR) system offers a more resilient approach to multimodal speaker identification, particularly in challenging conditions like missing data or language mismatches.
- · AI agent developers
- · Security sectors
- · Speech technology companies
- · Multimodal AI research
- · Systems reliant on single-modality identification
- · Those vulnerable to spoofing attacks
Improved accuracy and robustness of polyglot speaker identification in complex, noisy environments.
Accelerated development and adoption of AI agents capable of nuanced human interaction and verification.
Enhanced security protocols and personalized user experiences across diverse multilingual and multimodal digital interfaces.
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