A Speaker Recognition Method Based on Dynamic Convolution with Dual Attention Mechanism

Deep neural networks have gained significant attention in text-independent speaker recognition tasks. However, due to the fixed parameters of traditional static convolutional neural networks, they cannot flexibly capture the variation in phonemes that are integral to speech sentences. To address this limitation, this paper proposes a channel-space attention-based dynamic convolutional speaker recognition method. This method employs dual-attention mechanisms to generate dynamic convolutional kernels, which improves the capture of phoneme variation information between different inputs in the speech signal. We conducted experiments using the TIMIT dataset to evaluate the proposed method's effectiveness in various network frameworks. Our results show that the best performance can be achieved when dynamic convolution is generated using four static convolutional kernels. Specifically, in the ResNet-34 framework, the Equal Error Rate (EER%) of the proposed method is improved by 31.1% over the static convolutional method CNN and by 20.3% over the single-attention dynamic convolutional method (DynamicConv). Additionally, the performance of the proposed method is enhanced in all other network frameworks. These findings demonstrate the effectiveness of the proposed method and the importance of considering phoneme variations in speaker recognition systems.