  With the rise of voice-activated applications, the need for speaker
recognition is rapidly increasing. The x-vector, an embedding approach based on
a deep neural network (DNN), is considered the state-of-the-art when proper
end-to-end training is not feasible. However, the accuracy significantly
decreases when recording conditions (noise, sample rate, etc.) are mismatched,
either between the x-vector training data and the target data or between
enrollment and test data. We introduce the Siamese x-vector Reconstruction
(SVR) for domain adaptation. We reconstruct the embedding of a higher quality
signal from a lower quality counterpart using a lean auxiliary Siamese DNN. We
evaluate our method on several mismatch scenarios and demonstrate significant
improvement over the baseline.
