...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >Increasing anti-spoofing protection in speaker verification using linear prediction
【24h】

Increasing anti-spoofing protection in speaker verification using linear prediction

机译：使用线性预测在说话者验证中增加防欺骗保护

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This article addresses the problem of anti-spoofing protection in an automatic speaker verification (ASV) system. An improved version of a previously proposed spoofing countermeasure is presented. The presented method is based on the analysis of linear prediction error that results from both short-and long-term prediction of the input speech signal. It was observed that non-natural speech signals, i.e., synthetic or converted speech, were predicted in a different way than genuine speech. Therefore, in contrast to the classical linear prediction analysis, where usually only the prediction coefficients are analyzed, in the proposed approach the residual (error) signals were examined. During this analysis, 23 various prediction parameters were extracted, such as the energy of the prediction error, prediction gains and temporal parameters related to the prediction error signals. Various binary classifiers were researched to separate human and spoof classes, however the support vector machines with radial basis function (SVM-RBF) yielded the best results. When tested on the corpora provided for the ASVspoof 2015 Challenge, the proposed countermeasure returned better results than the previous version of the algorithm and, in most of the cases, the baseline spoofing detector based on the local binary patterns (LBP). It is hoped that the proposed method can be part of a generalized spoofing countermeasure helping to increase security of ASV systems.
机译：本文解决了自动扬声器验证（ASV）系统中的反欺骗保护问题。提出了以前提出的欺骗对策的改进版本。所提出的方法基于对线性预测误差的分析，该线性预测误差是由输入语音信号的短期和长期预测产生的。据观察，非自然语音信号，即合成或转换的语音，是用与真实语音不同的方式预测的。因此，与通常仅分析预测系数的经典线性预测分析相反，在提出的方法中，检查了残留（误差）信号。在该分析期间，提取了23个各种预测参数，例如预测误差的能量，预测增益和与预测误差信号有关的时间参数。研究了各种二进制分类器以区分人类和欺骗类，但是具有径向基函数的支持向量机（SVM-RBF）产生了最佳结果。在针对2015年ASVspoof挑战赛提供的语料库上进行测试时，所提出的对策比以前版本的算法以及在大多数情况下基于本地二进制模式（LBP）的基线欺骗检测器返回了更好的结果。希望所提出的方法可以成为有助于提高ASV系统安全性的通用欺骗对策的一部分。

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