Abstract: 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.

Abstract: 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.

Abstract: 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 dis

Abstract: 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.

Abstract: 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.

Abstract: 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 dis