  Attending to the speech stream of interest in multi-talker environments can
be a challenging task, particularly for listeners with hearing impairment.
Research suggests that neural responses assessed with electroencephalography
(EEG) are modulated by listener`s auditory attention, revealing selective
neural tracking (NT) of the attended speech. NT methods mostly rely on
hand-engineered acoustic and linguistic speech features to predict the neural
response. Only recently, deep neural network (DNN) models without specific
linguistic information have been used to extract speech features for NT,
demonstrating that speech features in hierarchical DNN layers can predict
neural responses throughout the auditory pathway. In this study, we go one step
further to investigate the suitability of similar DNN models for speech to
predict neural responses to competing speech observed in EEG. We recorded EEG
data using a 64-channel acquisition system from 17 listeners with normal
hearing instructed to attend to one of two competing talkers. Our data revealed
that EEG responses are significantly better predicted by DNN-extracted speech
features than by hand-engineered acoustic features. Furthermore, analysis of
hierarchical DNN layers showed that early layers yielded the highest
predictions. Moreover, we found a significant increase in auditory attention
classification accuracies with the use of DNN-extracted speech features over
the use of hand-engineered acoustic features. These findings open a new avenue
for development of new NT measures to evaluate and further advance hearing
technology.
