发明授权
US06263309B1 Maximum likelihood method for finding an adapted speaker model in eigenvoice space
失效
在本征语音空间中找到适应的说话者模型的最大似然法
- 专利标题: Maximum likelihood method for finding an adapted speaker model in eigenvoice space
- 专利标题(中): 在本征语音空间中找到适应的说话者模型的最大似然法
-
申请号: US09070054申请日: 1998-04-30
-
公开(公告)号: US06263309B1公开(公告)日: 2001-07-17
- 发明人: Patrick Nguyen , Roland Kuhn , Jean-Claude Junqua
- 申请人: Patrick Nguyen , Roland Kuhn , Jean-Claude Junqua
- 主分类号: G10L1508
- IPC分类号: G10L1508
摘要:
A set of speaker dependent models is trained upon a comparatively large number of training speakers, one model per speaker, and model parameters are extracted in a predefined order to construct a set of supervectors, one per speaker. Principle component analysis is then performed on the set of supervectors to generate a set of eigenvectors that define an eigenvoice space. If desired, the number of vectors may be reduced to achieve data compression. Thereafter, a new speaker provides adaptation data from which a supervector is constructed by constraining this supervector to be in the eigenvoice space based on a maximum likelihood estimation. The resulting coefficients in the eigenspace of this new speaker may then be used to construct a new set of model parameters from which an adapted model is constructed for that speaker. Environmental adaptation may be performed by including environmental variations in the training data.