Dilated residual networks with multi-level attention for speaker verification

• Published in: Neurocomputing (Amsterdam) Vol. 412; pp. 177 - 186
• Main Authors: Wu, Yanfeng, Guo, Chenkai, Gao, Hongcan, Xu, Jing, Bai, Guangdong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.10.2020
• Subjects: Dilated residual networks; Multi-level attention; Speaker verification; Speaker verification; Dilated residual networks; Multi-level attention
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2020.06.079

With the development of deep learning techniques, speaker verification (SV) systems based on deep neural network (DNN) achieve competitive performance compared with traditional i-vector-based works. Previous DNN-based SV methods usually employ time-delay neural network, limiting the extension of the network for an effective representation. Besides, existing attention mechanisms used in DNN-based SV systems are only applied to a single level of network architectures, leading to insufficiently extraction of important features. To address above issues, we propose an effective deep speaker embedding architecture for SV, which combines a residual connection of one-dimensional dilated convolutional layers, called dilated residual networks (DRNs), with a multi-level attention model. The DRNs can not only capture long time-frequency context information of features, but also exploit information from multiple layers of DNN. In addition, the multi-level attention model, which consists of two-dimensional convolutional block attention modules employed at the frame level and the vector-based attention utilized at the pooling layer, can emphasize important features at multiple levels of DNN. Experiments conducted on NIST SRE 2016 dataset show that the proposed architecture achieves a superior equal error rate (EER) of 7.094% and a better detection cost function (DCF16) of 0.552 compared with state-of-the-art methods. Furthermore, the ablation experiments demonstrate the effectiveness of dilated convolutions and the multi-level attention on SV tasks.