高技术通讯2011，Vol.21Issue(4)：386-391,6.DOI:10.3772/j.issn.1002-0470.2011.04.010
极低错误接受率的说话人识别方法研究
Study on a speaker recognition method with very low false acceptance
摘要
Abstract
Seeing that some speaker recognition methods have to deal with massive non-target speech data when in application and a rather low false acceptance rate of them can cause a large number of recognition errors, the authors of the paper carried out the research to develop a speaker recognition technique with the very low false acceptance rate of less than 0.0001 under the condition of guaranteeing a reasonable recall rate. The key of the work was to improve the Gauss mixture model (GMM)-uniform background model (UBM) based method by adding a verification module to further avoid false recognition of non-target data. Three verification methods, respectively based on coarse-grained analysis window,non-target competing model, and statistic vector of change status, were investigated. The experimental results showed that they all reduced false alarms effectively and the best performance was given by the method based on statistic vector of change status.关键词
说话人识别/极低错误接受率/粗粒度分析窗/集外竞争模型/变化状态统计矢量Key words
speaker recognition/ very low false acceptance rote/ coarse-grained analysis window/ non-target competing model/ statistic vector of change status引用本文复制引用
姜涛,韩纪庆,郑铁然,张光成..极低错误接受率的说话人识别方法研究[J].高技术通讯,2011,21(4):386-391,6.基金项目
973计划(2007CB311104)资助项目. （2007CB311104）
