Deep Speaker Vectors for Semi Text-independent Speaker Verification
TABLE II: EER results with additional data
['Phrase', 'Training', 'EER% Cosine', 'EER% LDA', 'EER% PLDA']
It can be observed that with more training data, the performance of d-vector systems is generally improved, despite that the extra data are recordings of other phrases. Another observation is that with more training data, the PLDA model tends to be less effective. This can be possibly explained by the fact that d-vectors are derived from activations of neural network units and so probably do not fit the linear Gaussian model that PLDA assumes.