Bimodal speaker identification using dynamic Bayesian network

被引：0
|
作者：
Li, DD [1 ]
Sang, LF [1 ]
Yang, YC [1 ]
Wu, ZH [1 ]
机构：
[1] Zhejiang Univ, Dept Comp Sci, Hangzhou, Peoples R China
来源：
ADVANCES IN BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS | 2004年 / 3338卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The authentication of a person requires a consistently high recognition accuracy which is difficult to attain using a single recognition modality. This paper assesses the fusion of voiceprint and face feature for bimodal speaker identification using Dynamic Bayesian Network (DBN). Our contribution is to propose a general feature-level fusion framework in bimodal speaker identification. Within the framework, the voice and face feature are combined into a single DBN to obtain better performance than any single system alone. The tests were conducted on a multi-modal database of 54 users who provided voiceprint and face data of different speech type and content We compare our approach with mono-modal system and other classic decision-level methods and show that feature-level fusion using dynamic Bayesian network improved performance by about 4-5%, much better than the others.
引用
收藏
页码：577 / 585
页数：9
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