Learning Speaker Embedding with Momentum Contrast
Table 6: Results on VoxCeleb1 trained on VoxCeleb1 dev set and all VoxCeleb2 data.
['method', 'EER (%)', 'minDCF0.01', 'minDCF0.001']
The same network architecture and training configuration as previous experiments are used, and no further hyper parameter tuning is conducted for this task. MoCo pretraining does help AAM training, while CE pretraining doesn’t Besides, the results confirms that embeddings learnned by AAM works better with cosine backend.