arXiv:2611.07485v1 Announce Type: new Abstract: Lyric intelligibility significantly impacts music engagement and comprehension, especially for hearing-impaired individuals. Existing speech intelligibility metrics perform poorly in musical contexts due to differences in prosody, timing, and spectral dynamics. We propose a unified multi-encoder framework that supports both invasive and non-invasive modeling for robust lyric intelligibility prediction. By capturing complementary local acoustic-semantic alignments, global musical semantics, and ASR-derived textual cues, our method achieves superior performance over baselines on the ICASSP 2026 Cadenza dataset, with the best model achieving 26.61 RMSE, a $9.2$\% improvement over the baseline, demonstrating its flexibility and robustness.