  Current hearing aids normally provide amplification based on a general
prescriptive fitting, and the benefits provided by the hearing aids vary among
different listening environments despite the inclusion of noise suppression
feature. Motivated by this fact, this paper proposes a data-driven machine
learning technique to develop hearing aid fittings that are customised to
speech in different noisy environments. A differentiable hearing loss model is
proposed and used to optimise fittings with back-propagation. The customisation
is reflected on the data of speech in different noise with also the
consideration of noise suppression. The objective evaluation shows the
advantages of optimised custom fittings over general prescriptive fittings.
