.png){kind=icon}
返回
Environmental Model Adaptation Based on Histogram Equalization
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2009.2014109.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, a new environmental model adaptation method is proposed for robust speech recognition under noisy environments. The proposed method adapts initial acoustic models of a speech recognizer into environmentally matched models by utilizing the histogram equalization technique. Experiments performed on the Aurora noisy environment showed that the proposed technique provides substantial improvement over the baseline speech recognizer trained on the clean speech data.
Keyword:
Environmental model adaptation
histogram equalization
robust speech recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
Empirical mode decomposition of digital mammograms for the statistical based characterization of architectural distortion数字乳腺X光图像的经验模态分解及其基于统计的建筑性结构变形特征描述
PREAI
分享收藏JESS Based Expert System Architecture for Diagnosis of Rice Plant Diseases: Design and Prototype Development基于JESS的稻瘟病诊断专家系统架构：设计与原型开发
PREAI
分享收藏
没有更多内容
没有更多内容