Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
721-725

Research on aggregation model in speaker recognition deep network

Deng Fei1a
Deng Lihong1a
Hu Wenyi1a
Zhang Gexiang1b,2
Yang Qiang2
1. a. School of Computer & Network Security(Oxford Brookes College), b. Artificial Intelligence Research Center, Chengdu University of Technology, Chengdu 610059, China
2. School of Control Engineering, Chengdu University of Information Technology, Chengdu 610059, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0391

Abstract

Speaker identification is an important biometric technology, and multiple deep convolutional neural network(DNN) -based model architectures have shown increasing feature representation capabilities and have resulted in unified end-to-end speaker identification systems that have achieved better performance than traditional recognition models. Among them, the speech level features aggregated by the aggregation model are one of the key factors affecting the accuracy of the speaker recognition system. Most current approaches use the self-attention pooling(SAP) aggregation model. However, SAP aggregation models often fail to perform frame selection accurately, and the aggregated speech level features are inaccurate and weakly robust. This paper constructed an improved aggregation model mSAP by introducing a mean vector approach to the aggregation approach of the SAP aggregation model. It worked in a more fine-grained and stable way to aggregate variable-length input sequences into discourse-level features, which could capture long-term changes in the input sequences more effectively. Experiments show that the equal error rate(EER) of the mSAP model decreases by 7.4, 1.75, and 0.24 compared to the TAP, SAP, and NetVLAD aggregation models, respectively, while the DCF values decrease by 0.018, 0.137, and 0.242 compared to these three aggregation models, respectively. The improved mSAP aggregation model is able to aggregate more robust and accurate discourse-level features effectively improving the performance of the end-to-end speaker recognition model.

Foundation Support

国家自然科学基金资助项目(61972324)
四川省科技计划资助项目(2021YFS0313,2021YFG0133)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0391
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 721-725
Serial Number: 1001-3695(2022)03-013-0721-05

Publish History

[2021-11-30] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

邓飞, 邓力洪, 胡文艺, 等. 说话人身份识别深度网络中的聚合模型研究 [J]. 计算机应用研究, 2022, 39 (3): 721-725. (Deng Fei, Deng Lihong, Hu Wenyi, et al. Research on aggregation model in speaker recognition deep network [J]. Application Research of Computers, 2022, 39 (3): 721-725. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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