Online feature compensation using modified quantile based noise estimation for robust speech recognition

被引：0
|
作者：
Lee, Heungkyu [1 ]
Kwon, Ohil [1 ]
Kim, June [1 ]
机构：
[1] Korea Univ, Dept Elect & Comp Engn, Seoul, South Korea
来源：
ADVANCES IN INTELLIGENT IT: ACTIVE MEDIA TECHNOLOGY 2006 | 2006年 / 138卷
关键词：
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：
This paper proposes online adaptation method using the modified quantile based noise estimation for feature compensation that is based on Gaussian mixture model for robust speech recognition interface. Proposed method is designed for active online model adaptation method to cope with varying environmental noise conditions. This method is compensated on logarithmic filter-bank energies domain, and modified quantile based noise estimation using beta-order harmonic mean is applied for online noise estimation. Experimental evaluation is done by using Aurora 2 database, and it showed that the proposed method is more robust than other comparative algorithms.
引用
收藏
页码：236 / 242
页数：7
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