ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks
Table 3: Ablation study of single system results on ASVspoof 2019. Due to space constraint, for each DNN model, we merely included top two performing systems in the table. Under Training Objective column, MCE stands for multi-class cross entropy, BCE stands for binary cross entropy, and acc/EER stands for the model selection criterion after each training epoch.
['Model', 'Acoustic Feature', 'Feature Engineering', 'Training Objective', 'Model Params.', 'PA development t-DCF [ITALIC] minnorm', 'PA development EER (%)', 'LA development t-DCF [ITALIC] minnorm', 'LA development EER (%)']
The first observation is that the i-vectors baseline performed worse than GMMs surprisingly, which is contrary to prior work on the ASVspoof 2017 corpus ASSERT attains substantial improvements from the baseline GMM and i-vectors systems on both PA and LA. In general, for training the proposed DNN models, logspec outperforms CQCC, and unified feature map with overlap outperforms without overlap and whole utterance. On the other hand, there are mixed results on using multi-task or binary training objective, and on model selection with dev EER or dev classification accuracy. We empirically found that the best single system is based on SENet34 trained with unified feature map with overlap of logspec and binary cross-entropy loss with dev accuracy model selection. The system obtains 92% and 94% relative improvements over CQCC-GMM on dev t-DCF and EER for PA, and 100% relative improvements for LA.