  This paper proposes neural networks for compensating sensorineural hearing
loss. The aim of the hearing loss compensation task is to transform a speech
signal to increase speech intelligibility after further processing by a person
with a hearing impairment, which is modeled by a hearing loss model. We propose
an interpretable model called dynamic processing network, which has a structure
similar to band-wise dynamic compressor. The network is differentiable, and
therefore allows to learn its parameters to maximize speech intelligibility.
More generic models based on convolutional layers were tested as well. The
performance of the tested architectures was assessed using spectro-temporal
objective index (STOI) with hearing-threshold noise and hearing aid speech
intelligibility (HASPI) metrics. The dynamic processing network gave a
significant improvement of STOI and HASPI in comparison to popular compressive
gain prescription rule Camfit. A large enough convolutional network could
outperform the interpretable model with the cost of larger computational load.
Finally, a combination of the dynamic processing network with convolutional
neural network gave the best results in terms of STOI and HASPI.
