  An accurate objective speech intelligibility prediction algorithms is of
great interest for many applications such as speech enhancement for hearing
aids. Most algorithms measures the signal-to-noise ratios or correlations
between the acoustic features of clean reference signals and degraded signals.
However, these hand-picked acoustic features are usually not explicitly
correlated with recognition. Meanwhile, deep neural network (DNN) based
automatic speech recogniser (ASR) is approaching human performance in some
speech recognition tasks. This work leverages the hidden representations from
DNN-based ASR as features for speech intelligibility prediction in
hearing-impaired listeners. The experiments based on a hearing aid
intelligibility database show that the proposed method could make better
prediction than a widely used short-time objective intelligibility (STOI) based
binaural measure.
