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1. 发明申请

WO2015134294A1 LOW-FOOTPRINT ADAPTATION AND PERSONALIZATION FOR A DEEP NEURAL NETWORK 审中-公开
标题翻译：用于深层神经网络的低自适应和个性化
公开(公告)号：WO2015134294A1
公开(公告)日：2015-09-11
申请号：PCT/US2015/017872
申请日：2015-02-27
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： XUE, Jian , LI, Jinyu , YU, Dong , SELTZER, Michael L. , GONG, Yifan
IPC分类号： G10L15/07 , G10L15/16
CPC分类号： G10L15/16 , G06N3/082 , G10L15/075
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.
摘要翻译：提供了一种用于自动语音识别的深层神经网络（DNN）模型的适应和个性化。可以在诸如语音搜索或短消息听写的ASR任务中接收包括用于一个或多个扬声器的语音特征的话语。然后可以将分解方法应用于DNN模型中的原始矩阵。响应于应用分解方法，原始矩阵可以被转换成小于原始矩阵的多个新矩阵。然后可以将正方形矩阵添加到新矩阵。然后可以将扬声器特定参数存储在方阵中。然后可以通过更新方阵来适应DNN模型。该过程可以应用于DNN模型中的所有原始矩阵。适应的DNN模型可以包括与原始DNN模型中接收的参数相比减少的参数数量。

2. 发明授权

EP3114680B1 LOW-FOOTPRINT ADAPTATION AND PERSONALIZATION FOR A DEEP NEURAL NETWORK 有权
公开(公告)号：EP3114680B1
公开(公告)日：2020-06-24
申请号：EP15717284.2
申请日：2015-02-27
申请人： Microsoft Technology Licensing, LLC
发明人： XUE, Jian , LI, Jinyu , YU, Dong , SELTZER, Michael L. , GONG, Yifan
IPC分类号： G06N3/08 , G10L15/07 , G10L15/16

3. 发明公开

EP3114680A1 LOW-FOOTPRINT ADAPTATION AND PERSONALIZATION FOR A DEEP NEURAL NETWORK 有权
标题翻译：适应和个性化的小体积FOR A深层神经网络
公开(公告)号：EP3114680A1
公开(公告)日：2017-01-11
申请号：EP15717284.2
申请日：2015-02-27
申请人： Microsoft Technology Licensing, LLC
发明人： XUE, Jian , LI, Jinyu , YU, Dong , SELTZER, Michael L. , GONG, Yifan
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.
摘要翻译：用于自动语音识别的深层神经网络（DNN）模型的适配和个性化设置。如语音搜索或短信听写：其中包括一个或多个扬声器的语音功能的话语可以在ASR任务接收。甲分解方法可接着在DNN模型被应用到原始矩阵。响应于施加的分解的方法中，原始矩阵可以被转换成多个新的矩阵，它们比原矩阵小。然后，方阵可被添加到新的矩阵。说话者特定参数然后可被存储在方阵。 DNN的模型然后可以通过更新方阵来适配。此过程可被应用到所有的数在DNN模型原始矩阵的。该angepasst DNN模型可以包括的参数比在原始DNN模型接收到的减少数目。

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