AMR: Adaptive Modality Routing for Multimodal Polyglot Speaker Identification
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 dynamically assesses per-sample input quality and i
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- FuzzySimilar title/name (fuzzy) · 59%pyannote/speaker-diarization-3.1 →
“Fuzzy title match (0.73): “AMR: Adaptive Modality Routing for Multimodal Polyglot Speak” ≈ “pyannote/speaker-diarization-3.1””
- PossiblePossibly related (embedding) · 52%lgy1027/matrix-live-diarizer →
- PossiblePossibly related (embedding) · 49%wq2012/awesome-diarization →
