发明申请
US20080201139A1 Generic framework for large-margin MCE training in speech recognition
有权
语言识别中大面积MCE培训的通用框架
- 专利标题: Generic framework for large-margin MCE training in speech recognition
- 专利标题(中): 语言识别中大面积MCE培训的通用框架
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申请号: US11708440申请日: 2007-02-20
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公开(公告)号: US20080201139A1公开(公告)日: 2008-08-21
- 发明人: Dong Yu , Alejandro Acero , Li Deng , Xiaodong He
- 申请人: Dong Yu , Alejandro Acero , Li Deng , Xiaodong He
- 申请人地址: US WA Redmond
- 专利权人: Microsoft Corporation
- 当前专利权人: Microsoft Corporation
- 当前专利权人地址: US WA Redmond
- 主分类号: G10L15/00
- IPC分类号: G10L15/00
摘要:
A method and apparatus for training an acoustic model are disclosed. A training corpus is accessed and converted into an initial acoustic model. Scores are calculated for a correct class and competitive classes, respectively, for each token given the initial acoustic model. Also, a sample-adaptive window bandwidth is calculated for each training token. From the calculated scores and the sample-adaptive window bandwidth values, loss values are calculated based on a loss function. The loss function, which may be derived from a Bayesian risk minimization viewpoint, can include a margin value that moves a decision boundary such that token-to-boundary distances for correct tokens that are near the decision boundary are maximized. The margin can either be a fixed margin or can vary monotonically as a function of algorithm iterations. The acoustic model is updated based on the calculated loss values. This process can be repeated until an empirical convergence is met.
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