Speaker verification method based on deep information divergence maximization

Chen CHEN; Yafeng RONG; Chaoqun JI; Deyun CHEN; Yongjun HE

doi:10.11959/j.issn.1000-436x.2021133

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Speaker verification method based on deep information divergence maximization

Correspondences | 更新时间：2024-06-05

- Speaker verification method based on deep information divergence maximization
- Journal on Communications Vol. 42, Issue 7, Pages: 231-237(2021)
- 作者机构：
  
  1. 哈尔滨理工大学计算机科学与技术学院，黑龙江哈尔滨 150080
  2. 哈尔滨理工大学计算机科学与技术博士后流动站，黑龙江哈尔滨 150080
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61673142);The Natural Science Foundation of Heilongjiang Province(JJ2019JQ0013);Heilongjiang Postdoctoral Fund(LBH-Z20020);The Fundamental Research Founds for the Central Universities of Heilongjiang Province(2020-KYYWF-0341)
- DOI：10.11959/j.issn.1000-436x.2021133
  CLC： TP391.4
- Online First：2021-07，
  
  Published：25 July 2021
- 稿件说明：
移动端阅览
Chen CHEN, Yafeng RONG, Chaoqun JI, et al. Speaker verification method based on deep information divergence maximization[J]. Journal on Communications, 2021, 42(7): 231-237.
DOI：

Chen CHEN, Yafeng RONG, Chaoqun JI, et al. Speaker verification method based on deep information divergence maximization[J]. Journal on Communications, 2021, 42(7): 231-237. DOI： 10.11959/j.issn.1000-436x.2021133.

摘要

针对说话人确认中无法准确捕获特征间非线性关系的问题，提出了一种基于深层信息散度最大化的目标函数表示方法。该方法能通过计算特征所在分布之间相似度，来对特征间的非线性关系进行隐性表示，并在最大化这种统计相关性的优化目标指导下，使深度神经网络向着同类数据更紧凑、异类数据更分散的方向优化，最终达到提升深层特征空间区分性的目标。实验结果表明，相对于其他深度学习方法，所提方法的相对等错误率（EER）最多降低了15.80%，显著提升了系统性能。

Abstract

To solve the problem that the nonlinear relationship between speaker representations cannot be accurately captured in speaker verification

an objective function based on depth information divergence maximization was proposed.It could implicitly represent the nonlinear relationship between speaker representations by calculating the similarity between their distributions.Under the supervision of the optimization goal of maximizing the statistical correlation

the deep neural network was optimized towards the direction that the within-class data was more compact and the between-class data were far away from each other

and finally the discrimination of deep speaker representation space could be effectively improved.Experimental results show that compared with other deep learning methods

the relative EER of the proposed method is reduced by 15.80% at most

which significantly improves the system performance.

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references

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