  With the increase in the availability of speech from varied domains, it is
imperative to use such out-of-domain data to improve existing speech systems.
Domain adaptation is a prominent pre-processing approach for this. We
investigate it for adapt microphone speech to the telephone domain.
Specifically, we explore CycleGAN-based unpaired translation of microphone data
to improve the x-vector/speaker embedding network for Telephony Speaker
Verification. We first demonstrate the efficacy of this on real challenging
data and then, to improve further, we modify the CycleGAN formulation to make
the adaptation task-specific. We modify CycleGAN's identity loss,
cycle-consistency loss, and adversarial loss to operate in the deep feature
space. Deep features of a signal are extracted from an auxiliary (speaker
embedding) network and, hence, preserves speaker identity. Our 3D
convolution-based Deep Feature Discriminators (DFD) show relative improvements
of 5-10% in terms of equal error rate. To dive deeper, we study a challenging
scenario of pooling (adapted) microphone and telephone data with data
augmentations and telephone codecs. Finally, we highlight the sensitivity of
CycleGAN hyper-parameters and introduce a parameter called probability of
adaptation.
