发明公开
EP3114680A1 LOW-FOOTPRINT ADAPTATION AND PERSONALIZATION FOR A DEEP NEURAL NETWORK
有权
适应和个性化的小体积FOR A深层神经网络
- 专利标题: LOW-FOOTPRINT ADAPTATION AND PERSONALIZATION FOR A DEEP NEURAL NETWORK
- 专利标题(中): 适应和个性化的小体积FOR A深层神经网络
-
申请号: EP15717284.2申请日: 2015-02-27
-
公开(公告)号: EP3114680A1公开(公告)日: 2017-01-11
- 发明人: XUE, Jian , LI, Jinyu , YU, Dong , SELTZER, Michael L. , GONG, Yifan
- 申请人: Microsoft Technology Licensing, LLC
- 申请人地址: One Microsoft Way Redmond, WA 98052-6399 US
- 专利权人: Microsoft Technology Licensing, LLC
- 当前专利权人: Microsoft Technology Licensing, LLC
- 当前专利权人地址: One Microsoft Way Redmond, WA 98052-6399 US
- 代理机构: Goddar, Heinz J.
- 优先权: US201414201704 20140307
- 国际公布: WO2015134294 20150911
- 主分类号: G10L15/07
- IPC分类号: G10L15/07 ; G10L15/16
摘要:
The adaptation and personalization of a deep neural network (DNN) model for automatic speech recognition is provided. An utterance which includes speech features for one or more speakers may be received in ASR tasks such as voice search or short message dictation. A decomposition approach may then be applied to an original matrix in the DNN model. In response to applying the decomposition approach, the original matrix may be converted into multiple new matrices which are smaller than the original matrix. A square matrix may then be added to the new matrices. Speaker-specific parameters may then be stored in the square matrix. The DNN model may then be adapted by updating the square matrix. This process may be applied to all of a number of original matrices in the DNN model. The adapted DNN model may include a reduced number of parameters than those received in the original DNN model.
公开/授权文献
信息查询