公开(公告)号：US11462212B2

公开(公告)日：2022-10-04

申请号：US16613304

申请日：2018-05-10

申请人： NIPPON TELEGRAPH AND TELEPHONE CORPORATION

发明人： Ryo Masumura ,  Hirokazu Masataki

IPC分类号： G10L15/18 ,  G06N3/08 ,  G10L15/16 ,  G10L15/197 ,  G10L15/22 ,  H04M3/42

摘要： A document identification device that improves class identification precision of multi-stream documents is provided. The document identification device includes: a primary stream expression generation unit that generates a primary stream expression, which is a fixed-length vector of a word sequence corresponding to each speaker's speech recorded in a setting including a plurality of speakers, for each speaker; a primary multi-stream expression generation unit that generates a primary multi-stream expression obtained by integrating the primary stream expression; a secondary stream expression generation unit that generates a secondary stream expression, which is a fixed-length vector generated based on the word sequence of each speaker and the primary multi-stream expression, for each speaker; and a secondary multi-stream expression generation unit that generates a secondary multi-stream expression obtained by integrating the secondary stream expression.

公开(公告)号：US11556783B2

公开(公告)日：2023-01-17

申请号：US16641779

申请日：2018-08-21

申请人： NIPPON TELEGRAPH AND TELEPHONE CORPORATION

发明人： Ryo Masumura ,  Hirokazu Masataki

IPC分类号： G06N3/08 ,  G06N3/04 ,  G10L15/16

摘要： There is provided a technique for transforming a confusion network to a representation that can be used as an input for machine learning. A confusion network distributed representation sequence generating part that generates a confusion network distributed representation sequence, which is a vector sequence, from an arc word set sequence and an arc weight set sequence constituting the confusion network is included. The confusion network distributed representation sequence generating part comprises: an arc word distributed representation set sequence transforming part that, by transforming an arc word included in the arc word set to a word distributed representation, obtains an arc word distributed representation set and generates an arc word distributed representation set sequence; and an arc word distributed representation set weighting/integrating part that generates the confusion network distributed representation sequence from the arc word distributed representation set sequence and the arc weight set sequence.

公开(公告)号：US11081105B2

公开(公告)日：2021-08-03

申请号：US16333156

申请日：2017-09-05

申请人： NIPPON TELEGRAPH AND TELEPHONE CORPORATION

发明人： Hirokazu Masataki ,  Taichi Asami ,  Takashi Nakamura ,  Ryo Masumura

IPC分类号： G10L15/16 ,  G06N99/00 ,  G06N3/08 ,  G10L15/06 ,  G10L15/065 ,  G10L15/183 ,  G06F40/30 ,  G06F40/216 ,  G06N3/04

摘要： A model learning device comprises: an initial value setting part that uses a parameter of a learned first model including a neural network to set a parameter of a second model including a neural network having a same network structure as the first model; a first output probability distribution calculating part that calculates a first output probability distribution including a distribution of an output probability of each unit on an output layer, using learning features and the first model; a second output probability distribution calculating part that calculates a second output probability distribution including a distribution of an output probability of each unit on the output layer, using learning features and the second model; and a modified model update part that obtains a weighted sum of a second loss function calculated from correct information and from the second output probability distribution, and a cross entropy between the first output probability distribution and the second output probability distribution, and updates the parameter of the second model so as to reduce the weighted sum.