Text-Independent Speaker Verification Based on Triplet Loss

被引：0
|
作者：
He, Junjie [1 ]
He, Jing [1 ]
Zhu, Liangjin [1 ]
机构：
[1] Commun Univ China, Sch Informat & Commun Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020) | 2020年
关键词：
Speaker Verification; LSTM; Triplet Loss;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An improved end-to-end text-independent speaker verification model is proposed in this paper. LSTM networks are employed to extract the speaker model embedding, and the triplet loss is used to optimize the training of the network which make the training of the speaker verification model more efficient while keep the computation complexity relatively low. With the triplet loss, the proposed model can achieve better EER performance.
引用
收藏
页码：2385 / 2388
页数：4
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