Abstract: In order to improve the recognition performance of voiceprint recognition technology, DenseNet is applied to the spectrogram to realize voiceprint recognition. DenseNet is optimized from two aspects: improving the computing efficiency of the network and enhancing the characterization ability of voiceprint features. Depth separable convolution is used to reduce the amount of network parameters, and center loss function item is increased to improve the characterization ability of voiceprint features. The training results show that, through the depth separable convolution, the network parameters are reduced by 25.5%, and the model size is reduced by 24.6%; The simulation results show that the increase of the center loss item makes the clustering effect of the voiceprint feature more obvious and improves the characterization ability of voiceprint features. Therefore, the improved DenseNet can achieve good recognition results in the field of spectrogram and voiceprint recognition.

Key words: voiceprint recognition, spectrogram, DenseNet, depth separable convolution, center loss