Reference-Aware SFM Layers for Intrusive Intelligibility Prediction
Abstract
Intrusive speech-intelligibility predictors that exploit explicit reference signals are now widespread, yet they have not consistently surpassed non-intrusive systems. We argue that a primary cause is the limited exploitation of speech foundation models (SFMs). This work revisits intrusive prediction by combining reference conditioning with multi-layer SFM representations. Our final system achieves a root mean squared error (RMSE) 22.36 on the development set and 24.98 on the evaluation set, ranking 1st on CPC3. These findings provide practical guidance for constructing SFM-based intrusive intelligibility predictors.
Fields of Study & Subjects
Ferroelectric and Negative Capacitance Devices
Advanced Memory and Neural Computing
Neural Networks and Applications
Publication details
- Journal / source
- ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Pages
- 14992–14996
- Publication date
- 21 April 2026
- Language
- EN