Invention Grant
US08423364B2 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
- Patent Title: Generic framework for large-margin MCE training in speech recognition
- Patent Title (中): 语言识别中大面积MCE培训的通用框架
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Application No.: US11708440Application Date: 2007-02-20
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Publication No.: US08423364B2Publication Date: 2013-04-16
- Inventor: Dong Yu , Alejandro Acero , Li Deng , Xiaodong He
- Applicant: Dong Yu , Alejandro Acero , Li Deng , Xiaodong He
- Applicant Address: US WA Redmond
- Assignee: Microsoft Corporation
- Current Assignee: Microsoft Corporation
- Current Assignee Address: US WA Redmond
- Agency: Westman, Champlin & Kelly, P.A.
- Main IPC: G10L15/14
- IPC: G10L15/14 ; G10L15/00 ; G10L15/06

Abstract:
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.
Public/Granted literature
- US20080201139A1 Generic framework for large-margin MCE training in speech recognition Public/Granted day:2008-08-21
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