  Current speaker recognition technology provides great performance with the
x-vector approach. However, performance decreases when the evaluation domain is
different from the training domain, an issue usually addressed with domain
adaptation approaches. Recently, unsupervised domain adaptation using
cycle-consistent Generative Adversarial Netorks (CycleGAN) has received a lot
of attention. CycleGAN learn mappings between features of two domains given
non-parallel data. We investigate their effectiveness in low resource scenario
i.e. when limited amount of target domain data is available for adaptation, a
case unexplored in previous works. We experiment with two adaptation tasks:
microphone to telephone and a novel reverberant to clean adaptation with the
end goal of improving speaker recognition performance. Number of speakers
present in source and target domains are 7000 and 191 respectively. By adding
noise to the target domain during CycleGAN training, we were able to achieve
better performance compared to the adaptation system whose CycleGAN was trained
on a larger target data. On reverberant to clean adaptation task, our models
improved EER by 18.3% relative on VOiCES dataset compared to a system trained
on clean data. They also slightly improved over the state-of-the-art Weighted
Prediction Error (WPE) de-reverberation algorithm.
