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Network pruning is of great importance due to the elimination of theunimportant weights or features activated due to the networkover-parametrization. Advantages of sparsity enforcement include preventing theoverfitting and speedup. Considering a large number of parameters in deeparchitectures, network compression becomes of critical importance due to therequired huge amount of computational power. In this work, we impose structuredsparsity for speaker verification which is the validation of the query speakercompared to the speaker gallery. We will show that the mere sparsityenforcement can improve the verification results due to the possible initialoverfitting in the network.

Sparse Architectures for Text-Independent Speaker Verification Using Deep Neural Networks