  Hearing aids are expected to improve speech intelligibility for listeners
with hearing impairment. An appropriate amplification fitting tuned for the
listener's hearing disability is critical for good performance. The
developments of most prescriptive fittings are based on data collected in
subjective listening experiments, which are usually expensive and
time-consuming. In this paper, we explore an alternative approach to finding
the optimal fitting by introducing a hearing aid speech processing framework,
in which the fitting is optimised in an automated way using an intelligibility
objective function based on the HASPI physiological auditory model. The
framework is fully differentiable, thus can employ the back-propagation
algorithm for efficient, data-driven optimisation. Our initial objective
experiments show promising results for noise-free speech amplification, where
the automatically optimised processors outperform one of the well recognised
hearing aid prescriptions.
