  In real-life conditions, mismatch between development and test domain
degrades speaker recognition performance. To solve the issue, many researchers
explored domain adaptation approaches using matched in-domain dataset. However,
adaptation would be not effective if the dataset is insufficient to estimate
channel variability of the domain. In this paper, we explore the problem of
performance degradation under such a situation of insufficient channel
information. In order to exploit limited in-domain dataset effectively, we
propose an unsupervised domain adaptation approach using Autoencoder based
Domain Adaptation (AEDA). The proposed approach combines an autoencoder with a
denoising autoencoder to adapt resource-rich development dataset to test
domain. The proposed technique is evaluated on the Domain Adaptation Challenge
13 experimental protocols that is widely used in speaker recognition for domain
mismatched condition. The results show significant improvements over baselines
and results from other prior studies.
