  In real-life applications, the performance of speaker recognition systems
always degrades when there is a mismatch between training and evaluation data.
Many domain adaptation methods have been successfully used for eliminating the
domain mismatches in speaker recognition. However, usually both training and
evaluation data themselves can be composed of several subsets. These inner
variances of each dataset can also be considered as different domains.
Different distributed subsets in source or target domain dataset can also cause
multi-domain mismatches, which are influential to speaker recognition
performance. In this study, we propose to use adversarial training for
multi-domain speaker recognition to solve the domain mismatch and the dataset
variance problems. By adopting the proposed method, we are able to obtain both
multi-domain-invariant and speaker-discriminative speech representations for
speaker recognition. Experimental results on DAC13 dataset indicate that the
proposed method is not only effective to solve the multi-domain mismatch
problem, but also outperforms the compared unsupervised domain adaptation
methods.
