We present LIWhiz, a non-intrusive lyric intelligibility prediction system submitted to the ICASSP 2026 Cadenza Challenge. LIWhiz leverages Whisper for robust feature extraction and a trainable back-end for score prediction. Tested on the Cadenza Lyric Intelligibility Prediction (CLIP) evaluation set, LIWhiz achieves a root mean square error (RMSE) of 27.07%, a 22.4% relative RMSE reduction over the STOI-based baseline, yielding a substantial improvement in normalized cross-correlation.
@article{arxiv.2512.17937,
title = {LIWhiz: A Non-Intrusive Lyric Intelligibility Prediction System for the Cadenza Challenge},
author = {Ram C. M. C. Shekar and Iván López-Espejo},
journal= {arXiv preprint arXiv:2512.17937},
year = {2026}
}