  Non-intrusive intelligibility prediction is important for its application in
realistic scenarios, where a clean reference signal is difficult to access. The
construction of many non-intrusive predictors require either ground truth
intelligibility labels or clean reference signals for supervised learning. In
this work, we leverage an unsupervised uncertainty estimation method for
predicting speech intelligibility, which does not require intelligibility
labels or reference signals to train the predictor. Our experiments demonstrate
that the uncertainty from state-of-the-art end-to-end automatic speech
recognition (ASR) models is highly correlated with speech intelligibility. The
proposed method is evaluated on two databases and the results show that the
unsupervised uncertainty measures of ASR models are more correlated with speech
intelligibility from listening results than the predictions made by widely used
intrusive methods.
