arXiv Open Access 2025

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

Ram C. M. C. Shekar Iván López-Espejo

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Abstrak

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.

Topik & Kata Kunci

eess.AS cs.SD

Penulis (2)

Ram C. M. C. Shekar

Iván López-Espejo

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APA MLA BibTeX

Shekar, R.C.M.C., López-Espejo, I. (2025). LIWhiz: A Non-Intrusive Lyric Intelligibility Prediction System for the Cadenza Challenge. https://arxiv.org/abs/2512.17937

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓