  Speech enhancement deep learning systems usually require large amounts of
training data to operate in broad conditions or real applications. This makes
the adaptability of those systems into new, low resource environments an
important topic. In this work, we present the results of adapting a speech
enhancement generative adversarial network by finetuning the generator with
small amounts of data. We investigate the minimum requirements to obtain a
stable behavior in terms of several objective metrics in two very different
languages: Catalan and Korean. We also study the variability of test
performance to unseen noise as a function of the amount of different types of
noise available for training. Results show that adapting a pre-trained English
model with 10 min of data already achieves a comparable performance to having
two orders of magnitude more data. They also demonstrate the relative stability
in test performance with respect to the number of training noise types.
