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arXiv · 2512.17937

LIWhiz: A Non-Intrusive Lyric Intelligibility Prediction System for the Cadenza Challenge

Ram C. M. C. Shekar·Iván López-Espejo
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Abstract

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.

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BibTeXRIS

Ram C. M. C. Shekar, Iván López-Espejo. 2025-12-11. LIWhiz: A Non-Intrusive Lyric Intelligibility Prediction System for the Cadenza Challenge. https://arxiv.org/abs/2512.17937

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