Speaker Verification on Small Datasets with ResNet50

In this study, we explore the capabilities of speaker recognition technology for biometric authentication, developing speaker recognition-based access control systems, and serving as a resource for future research. We focused on developing and evaluating the ResNet50 model for speaker verification. The model was trained and tested on private datasets with 32 speakers and public datasets with 1251 to 6112 speakers. The model ResNet50 achieved a good result on our private dataset by achieving the best EER of 0.7%.