Invention Grant
- Patent Title: Low-footprint adaptation and personalization for a deep neural network
- Patent Title (中): 深层神经网络的低空间适应和个性化
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Application No.: US14201704Application Date: 2014-03-07
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Publication No.: US09324321B2Publication Date: 2016-04-26
- Inventor: Jian Xue , Jinyu Li , Dong Yu , Michael L. Seltzer , Yifan Gong
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agent Tom Wong; Steven Spellman; Micky Minhas
- Main IPC: G10L15/16
- IPC: G10L15/16 ; G10L15/07

Abstract:
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.
Public/Granted literature
- US20150255061A1 LOW-FOOTPRINT ADAPTATION AND PERSONALIZATION FOR A DEEP NEURAL NETWORK Public/Granted day:2015-09-10
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