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发明授权
US06519561B1 Model adaptation of neural tree networks and other fused models for speaker verification 有权
模型适应神经树网络和其他融合模型进行说话人验证

  • 专利标题： Model adaptation of neural tree networks and other fused models for speaker verification
  • 专利标题（中）： 模型适应神经树网络和其他融合模型进行说话人验证
  • 申请号： US09185871
    申请日： 1998-11-03
  • 公开(公告)号： US06519561B1
    公开(公告)日： 2003-02-11
  • 发明人: Kevin Farrell , William Mistretta
  • 申请人： Kevin Farrell , William Mistretta
  • 主分类号： G10L1700
  • IPC分类号： G10L1700
Model adaptation of neural tree networks and other fused models for speaker verification
摘要：
The model adaptation system of the present invention is a speaker verification system that embodies the capability to adapt models learned during the enrollment component to track aging of a user's voice. The system has the advantage of only requiring a single enrollment for the user. The model adaptation system and methods can be applied to several types of speaker recognition models including neural tree networks (NTN), Gaussian Mixture Models (GMMs), and dynamic time warping (DTW) or to multiple models (i.e., combinations of NTNs, GMMs and DTW). Moreover, the present invention can be applied to text-dependent or text-independent systems.
摘要（中）：

本发明的模型适应系统是一种扬声器验证系统，其体现了适应在注册组件期间学习的模型以跟踪用户语音的老化的能力。 该系统的优点是只需要用户单次注册。 模型适应系统和方法可以应用于包括神经树网络（NTN），高斯混合模型（GMM）和动态时间扭曲（DTW）或多个模型（即NTN，GMM的组合） 和DTW）。 此外，本发明可以应用于文本依赖或文本无关系统。

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