  Facial recognition system is one of the major successes of Artificial
intelligence and has been used a lot over the last years. But, images are not
the only biometric present: audio is another possible biometric that can be
used as an alternative to the existing recognition systems. However, the
text-independent audio data is not always available for tasks like speaker
verification and also no work has been done in the past for text-independent
speaker verification assuming very little training data. Therefore, In this
paper, we have proposed a novel method to verify the identity of the claimed
speaker using very few training data. To achieve this we are using a Siamese
neural network with center loss and speaker bias loss. Experiments conducted on
the VoxCeleb1 dataset show that the proposed model accuracy even on training
with very few data is near to the state of the art model on text-independent
speaker verification
