  Deep learning approaches are still not very common in the speaker
verification field. We investigate the possibility of using deep residual
convolutional neural network with spectrograms as an input features in the
text-dependent speaker verification task. Despite the fact that we were not
able to surpass the baseline system in quality, we achieved a quite good
results for such a new approach getting an 5.23% ERR on the RSR2015 evaluation
part. Fusion of the baseline and proposed systems outperformed the best
individual system by 18% relatively.
