The automatic speaker verification spoofing (ASVspoof) challenge series
is crucial for enhancing the spoofing consideration and the
countermeasures growth. Although the recent ASVspoof 2019 validation
results indicate the significant capability to identify most attacks,
the model’s recognition effect is still poor for some attacks. This
paper presents the Online Hard Example Mining (OHEM) algorithm for
detecting unknown voice spoofing attacks. The OHEM is utilized to
overcome the imbalance between simple and hard samples in the dataset.
The presented system provides an equal error rate (EER) of 0.77% on the
ASVspoof 2019 Challenge logical access scenario’s evaluation set.