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

Language and Noise Transfer in Speech Enhancement Generative Adversarial Network