Speaker Verification Method Based on Sub-band Front-end Model and Inverse Feature Fusion

Two problems with time delay neural networks(TDNN) used to extract frame-level features in existing speaker confirmation methods are the lack of the ability to model local frequency features and the inability of the multilayer feature fusion approach to effectively model the complex relationships between high-level and low-level features.Therefore,a new front-end model as well as a new multilayer feature fusion approach are proposed.In the front-end model,by dividing the input feature map into multiple sub-bands and expanding the frequency range of the sub-bands layer by layer,the TDNN can model the local frequency features progressively.Meanwhile,a new inverse path passing from higher to lower layers is added to the backbone model to model the relationship between the output features of two adjacent layers,and the outputs of each layer in the inverse path are concatenated to serve as the fused features.In addition,the design of the inverse bottleneck layer is used in the backbone model to further improve the performance of the model.Experimental results on the VoxCeleb1 test set show that the proposed method has a relative reduction of 9% in the equal error rate and 14% in the minimum cost detection function,compared to the current TDNN method,while the number of parameters is only 52% of the current method.