Learning Speaker Embedding with Momentum Contrast
Table 5: Results of AAM trained models using different pretraining strategy. CE, Moco, MoCo-extra stands for, respectively,cross entropy pretraining, pretraining with Dataset A, and with both Dataset A and Dataset B. Cosine backend is applied for all models
['method', 'EER (%)', 'minDCF0.01', 'minDCF0.001']
In this section, we study if the encoder trained with MoCo is helpful for downstream supervised learning. We initialize x-vector with the encoder pretrained by MoCo in Sec. As comparison, we also conduct experiment with conventional pretraining with cross entropy training (as used in [zeinali2019but]). The EER improves from 1.44% to 1.242%, a 13.7% relative improvement. At the same time, we find that no improvement is observed with cross entropy pretraining. Cross entropy pretraining does not transfer well to the downstream AAM training. In fact it is harmful for AAM training in our case. One possible explanation is that both MoCo and AAM encourage the representation to hyper-sphere subspace (via softmax over dot product), while cross entropy learns a difference subspace.