  This paper describes two intelligibility prediction systems derived from a
pretrained noise-robust automatic speech recognition (ASR) model for the second
Clarity Prediction Challenge (CPC2). One system is intrusive and leverages the
hidden representations of the ASR model. The other system is non-intrusive and
makes predictions with derived ASR uncertainty. The ASR model is only
pretrained with a simulated noisy speech corpus and does not take advantage of
the CPC2 data. For that reason, the intelligibility prediction systems are
robust to unseen scenarios given the accurate prediction performance on the
CPC2 evaluation.
