2026
REFERENCE-AWARE SFM LAYERS FOR INTRUSIVE INTELLIGIBILITY PREDICTION
ICASSP 2026poster
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 predictio…