  Domain adaptation plays an important role for speech recognition models, in
particular, for domains that have low resources. We propose a novel generative
model based on cyclic-consistent generative adversarial network (CycleGAN) for
unsupervised non-parallel speech domain adaptation. The proposed model employs
multiple independent discriminators on the power spectrogram, each in charge of
different frequency bands. As a result we have 1) better discriminators that
focus on fine-grained details of the frequency features, and 2) a generator
that is capable of generating more realistic domain-adapted spectrogram. We
demonstrate the effectiveness of our method on speech recognition with gender
adaptation, where the model only has access to supervised data from one gender
during training, but is evaluated on the other at test time. Our model is able
to achieve an average of $7.41\%$ on phoneme error rate, and $11.10\%$ word
error rate relative performance improvement as compared to the baseline, on
TIMIT and WSJ dataset, respectively. Qualitatively, our model also generates
more natural sounding speech, when conditioned on data from the other domain.
