Abstract: A spoken dialogue device includes a recognition unit that recognizes an acquired user speech, a barge-in speech control unit that determines whether to engage a barge-in speech, a dialogue control unit that outputs a system response to a user based on a recognition result of the user speech other than the barge-in speech determined not to be engaged by the barge-in speech control unit, a response generation unit that generates a system speech based on the system response, and an output unit that outputs a system speech. When each user speech element included in the user speech corresponds to a predetermined morpheme included in the immediately previous system speech and does not correspond to a response candidate to the immediately previous system speech by a user, the barge-in speech control unit does not engage at least the user speech element.

Abstract: A voice data creation device is a device configured to create voice data including an additional word which is a word to be added to a recognition target in a speech recognition system, and includes: a sentence example extraction unit configured to extract one or more text corpora including the additional word from a text corpus group including a plurality of text corpora consisting of sentence examples including a plurality of words; a sentence example selection unit configured to select a text corpus having a highest measure indicating a likelihood of occurrence as a sentence among the text corpora extracted by the sentence example extraction unit 11 as an optimal sentence example for the additional word; and a voice creation unit configured to output a synthesized voice of the optimal sentence example generated by a predetermined voice synthesis system as voice data corresponding to the additional word.

Abstract: A word weight calculation system is a system that calculates the weight of an additional word registered in a word dictionary used for speech recognition, and includes: a text acquisition unit configured to acquire a combination of a speech recognition result text, which is a result of speech recognition using a word dictionary including an additional word with a predetermined weight set in advance, and a correct text, which is a correct answer for the speech recognition, the combination including the additional word in any of the texts; and a weight calculation unit configured to calculate the weight of the additional word according to an erroneous word corresponding to the additional word included in any of the acquired texts, and a preset number of preceding words before the additional word or the erroneous word included in the correct text.

Abstract: A spoken dialogue device includes a recognition unit that recognizes an acquired user speech, a barge-in speech control unit that determines whether to engage a barge-in speech, a dialogue control unit that outputs a system response to a user based on a recognition result of the user speech other than the barge-in speech determined not to be engaged by the barge-in speech control unit, a response generation unit that generates a system speech based on the system response, and an output unit that outputs a system speech. When each user speech element included in the user speech corresponds to a predetermined morpheme included in the immediately previous system speech and does not correspond to a response candidate to the immediately previous system speech by a user, the barge-in speech control unit does not engage at least the user speech element.

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: An acoustic model learning apparatus includes a first output probability distribution calculating part that calculates a first output probability distribution including a distribution of output probabilities of respective units of an output layer using a feature amount obtained from an acoustic signal for learning and a learned first acoustic model including a neural network, and the first output probability distribution calculating part obtains the first output probability distribution using a smoothing parameter made up of a real value greater than 0 as input so that the first output probability distribution approaches a uniform distribution as the smoothing parameter is greater, and calculates the first output probability distribution by obtaining logits of respective units of an output layer using the feature amount obtained from the acoustic signal for learning and the first acoustic model and setting a value of the smoothing parameter greater in the case where an output unit number with the greatest log

Abstract: An acoustic model learning apparatus includes a first output probability distribution calculating part that calculates a first output probability distribution including a distribution of output probabilities of respective units of an output layer using a feature amount obtained from an acoustic signal for learning and a learned first acoustic model including a neural network, and the first output probability distribution calculating part obtains the first output probability distribution using a smoothing parameter made up of a real value greater than 0 as input so that the first output probability distribution approaches a uniform distribution as the smoothing parameter is greater, and calculates the first output probability distribution by obtaining logits of respective units of an output layer using the feature amount obtained from the acoustic signal for learning and the first acoustic model and setting a value of the smoothing parameter greater in the case where an output unit number with the greatest log

Abstract: Provided is a word vectorization device that converts a word to a word vector considering the acoustic feature of the word. A word vectorization model learning device comprises a learning part for learning a word vectorization model by using a vector wL,s(t) indicating a word yL,s(t) included in learning text data, and an acoustic feature amount afL,s(t) that is an acoustic feature amount of speech data corresponding to the learning text data and that corresponds to the word yL,s(t). The word vectorization model includes a neural network that receives a vector indicating a word as an input and outputs the acoustic feature amount of speech data corresponding to the word, and the word vectorization model is a model that uses an output value from any intermediate layer as a word vector.

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