  Typically, neural network-based speech dereverberation models are trained on
paired data, composed of a dry utterance and its corresponding reverberant
utterance. The main limitation of this approach is that such models can only be
trained on large amounts of data and a variety of room impulse responses when
the data is synthetically reverberated, since acquiring real paired data is
costly. In this paper we propose a CycleGAN-based approach that enables
dereverberation models to be trained on unpaired data. We quantify the impact
of using unpaired data by comparing the proposed unpaired model to a paired
model with the same architecture and trained on the paired version of the same
dataset. We show that the performance of the unpaired model is comparable to
the performance of the paired model on two different datasets, according to
objective evaluation metrics. Furthermore, we run two subjective evaluations
and show that both models achieve comparable subjective quality on the AMI
dataset, which was not seen during training.
