Overview of the ICASSP 2026 Cadenza Challenge: Predicting Lyric Intelligibility
Abstract
We present the first open challenge on predicting lyric intelligibility. A new dataset, CLIP1, was introduced, comprising audio samples of popular western music paired with listener intelligibility scores. To model diverse listening profiles, samples were processed with no, mild and moderate simulated hearing loss. A total of 27 systems were submitted by 22 teams. Most systems used foundation models to extract encoder embeddings as high-level acoustic representations, often complemented by signal features and perceptual metrics. Twenty-five systems outperformed the STOI baseline, and 16 outperformed a Whisper-based baseline.
Fields of Study & Subjects
Machine Learning in Materials Science
Music Technology and Sound Studies
Phonetics and Phonology Research
Publication details
- Journal / source
- ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Pages
- 21757–21759
- Publication date
- 21 April 2026
- Language
- EN