  This work focuses on online dereverberation for hearing devices using the
weighted prediction error (WPE) algorithm. WPE filtering requires an estimate
of the target speech power spectral density (PSD). Recently deep neural
networks (DNNs) have been used for this task. However, these approaches
optimize the PSD estimate which only indirectly affects the WPE output, thus
potentially resulting in limited dereverberation. In this paper, we propose an
end-to-end approach specialized for online processing, that directly optimizes
the dereverberated output signal. In addition, we propose to adapt it to the
needs of different types of hearing-device users by modifying the optimization
target as well as the WPE algorithm characteristics used in training. We show
that the proposed end-to-end approach outperforms the traditional and
conventional DNN-supported WPEs on a noise-free version of the WHAMR! dataset.
