  Most speaker verification tasks are studied as an open-set evaluation
scenario considering the real-world condition. Thus, the generalization power
to unseen speakers is of paramount important to the performance of the speaker
verification system. We propose to apply \textit {Mean Teacher}, a temporal
averaging model, to extract speaker embeddings with small intra-class variance
and large inter-class variance. The mean teacher network refers to the temporal
averaging of deep neural network parameters; it can produces more accurate and
stable representations than using weights after the training finished. By
learning the reliable intermediate representation of the mean teacher network,
we expect that the proposed method can explore more discriminatory embedding
spaces and improve the generalization performance of the speaker verification
system. Experimental results on the VoxCeleb1 test set demonstrate that the
proposed method relatively improves performance by 11.61\%, compared to a
baseline system.
