Speaker adaptations in sparse training data for improved speaker verification

被引：5
|
作者：
Ahn, S [1 ]
Ko, H [1 ]
机构：
[1] Korea Univ, Dept Elect Engn, Sungbuk Ku, Seoul 136701, South Korea
来源：
ELECTRONICS LETTERS | 2000年 / 36卷 / 04期
关键词：
D O I：
10.1049/el:20000330
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The over-training problem in speaker verification occurs when modelling a speaker with sparse training data. The authors propose to solve this problem by employing effective speaker adaptations using a hybrid version of the maximum a posteriori (MAP) and maximum likelihood linear regression (MLLR) methods. Experimental results show that the speaker Verification system using the proposed hybrid adaptation scheme outperforms systems based on speaker models without adaptation by a factor of up to 5.
引用
收藏
页码：371 / 373
页数：3
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