  Current deep learning (DL) based approaches to speech intelligibility
enhancement in noisy environments are generally trained to minimise the
distance between clean and enhanced speech features. These often result in
improved speech quality however they suffer from a lack of generalisation and
may not deliver the required speech intelligibility in everyday noisy
situations. In an attempt to address these challenges, researchers have
explored intelligibility-oriented (I-O) loss functions to train DL approaches
for robust speech enhancement (SE). In this paper, we formulate a novel
canonical correlation-based I-O loss function to more effectively train DL
algorithms. Specifically, we present a fully convolutional SE model that uses a
modified canonical-correlation based short-time objective intelligibility
(CC-STOI) metric as a training cost function. To the best of our knowledge,
this is the first work that exploits the integration of canonical correlation
in an I-O based loss function for SE. Comparative experimental results
demonstrate that our proposed CC-STOI based SE framework outperforms DL models
trained with conventional STOI and distance-based loss functions, in terms of
both standard objective and subjective evaluation measures when dealing with
unseen speakers and noises.
