Learning Speaker Embedding with Momentum Contrast
Table 3: Performance for the baseline systems.
['method', 'ivector (cosine)', 'ivector (PLDA)', 'xvector-CE (PLDA)', 'xvector-AAM (PLDA)', 'xvector-AAM (cosine)']
It can be found that AAM can achieve better performances than cross entropy based training, and this observation is in line with findings by [xiang2019aam, zeinali2019but]. Direct cosine distance can achieve decent performance, and a separate PLDA backend contribute no improvement (though no significant performance degradation w.r.t EER, is surpassed by cosine on minDCF criterion by large margins). Thus, we use AAM-loss trained network with cosine backend for future comparison.