Neural models, in particular the d-vector and x-vector architectures, have produced state-of-the-art performance on many speaker verification tasks. However, two potential problems of these neural ...

Using larger scale datasets in the training stage of speaker verification model usually leads to better performance. However, when the speaker number of the training dataset becomes extreme large (e.g ...