1 research outputs found
Sparse Architectures for Text-Independent Speaker Verification Using Deep Neural Networks
Network pruning is of great importance due to the elimination of the
unimportant weights or features activated due to the network
over-parametrization. Advantages of sparsity enforcement include preventing the
overfitting and speedup. Considering a large number of parameters in deep
architectures, network compression becomes of critical importance due to the
required huge amount of computational power. In this work, we impose structured
sparsity for speaker verification which is the validation of the query speaker
compared to the speaker gallery. We will show that the mere sparsity
enforcement can improve the verification results due to the possible initial
overfitting in the network