• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on measuring technology and mechatronics automation >A New Method Based on HMMs and K-means Algorithms for Noise-Robust Voice Activity Detector
【24h】

A New Method Based on HMMs and K-means Algorithms for Noise-Robust Voice Activity Detector

机译：基于HMM和K-means算法的鲁棒语音活动检测器新方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we proposed left-right hidden Markov models (HMMs) combination with k-means threshold of Likelihood ratio test (LRT) to identify the start and end of the speech. This method builds two models of non-speech and speech but not two states, i.e. each model could conclude several states. In the experiments we present the Voice Activity Detection (VAD) results between two states hidden semi-Markov model (HSMM) and proposed algorithm. We also compare accuracy and robust between the k-means threshold and the adaptive threshold in high signal to noise rate in the background noise. It presents that k-means threshold is more effective than the adaptive threshold and the proposed method also make a better performance than two states HSMM based VAD, especially in the low signal-to-noise ratio(SNR) environment.
机译：在本文中，我们提出了带有k均值阈值的似然比检验（LRT）的左右隐马尔可夫模型（HMM）组合，以识别语音的开始和结束。该方法建立了两个非语音和语音模型，但没有建立两个状态，即每个模型都可以得出多个状态。在实验中，我们提出了两种状态隐藏半马尔可夫模型（HSMM）之间的语音活动检测（VAD）结果和提出的算法。我们还比较了背景噪声中高信噪比的k均值阈值和自适应阈值之间的准确性和鲁棒性。结果表明，k均值阈值比自适应阈值更有效，并且所提出的方法也比基于两种状态HSMM的VAD具有更好的性能，尤其是在低信噪比（SNR）环境中。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号