  Speech foundation models (SFMs) have been benchmarked on many speech
processing tasks, often achieving state-of-the-art performance with minimal
adaptation. However, the SFM paradigm has been significantly less explored for
applications of interest to the speech perception community. In this paper we
present a systematic evaluation of 10 SFMs on one such application: Speech
intelligibility prediction. We focus on the non-intrusive setup of the Clarity
Prediction Challenge 2 (CPC2), where the task is to predict the percentage of
words correctly perceived by hearing-impaired listeners from speech-in-noise
recordings. We propose a simple method that learns a lightweight specialized
prediction head on top of frozen SFMs to approach the problem. Our results
reveal statistically significant differences in performance across SFMs. Our
method resulted in the winning submission in the CPC2, demonstrating its
promise for speech perception applications.
