MG-REPA: Multi-Granularity Representation Alignment for Generative Target Speaker Extraction

MG-REPA introduces Multi-Granularity Representation Alignment for generative target speaker extraction, aligning hidden states with a self‑supervised learning model at the frame level and a speaker verification model at the utterance level. Temporal normalization removes utterance‑level statistics from the frame‑level target, allowing each branch to focus on its specific granularity. On Libri2Mix, MG-REPA achieves comparable or better performance to a 2,000‑epoch baseline while requiring only 750 epochs, and ablation studies confirm its superiority over single‑granularity alignment.

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• llms
• diffusion
• benchmarks
• safety