Nippon Telegraph and Telephone Corporation

Astellas Pharma Gmbh

Ko Tanaka

Hirokazu Kameoka

Takuhiro Kaneko

Nobukatsu Hojo

Yasunori Oishi

Masakazu Miyazaki

Seiji Takae

Akihiko Sasaki

Aaron Valero Puche

A voice signal conversion model learning device includes: a generation unit configured to execute generation processing of generating a conversion destination voice signal on the basis of an input voice signal that is a voice signal of an input voice, conversion source attribute information that is information indicating an attribute of an input voice that is a voice represented by the input voice signal, and conversion destination attribute information indicating an attribute of a voice represented by the conversion destination voice signal that is a voice signal of a conversion destination of the input voice signal; and an identification unit configured to execute voice estimation processing of estimating whether or not a voice signal that is a processing target is a voice signal representing a vocal sound actually uttered by a person on the basis of the conversion source attribute information and the conversion destination attribute intonation, wherein the conversion destination voice signal is input to the identification unit, the processing target is a voice signal input to the identification unit, and the generation unit and the identification unit pertain learning on the basis of an estimation result of the voice estimation processing.

Audio signal conversion model learning apparatus, audio signal conversion apparatus, audio signal conversion model learning method and program

A voice conversion device is provided with a linguistic information extraction unit that extracts linguistic information corresponding to utterance content from a conversion source voice signal, an appearance feature extraction unit that extracts appearance features expressing features related to the look of a person's face from a captured image of the person, and a converted voice generation unit that generates a converted voice on a basis of the linguistic information and the appearance features.

Voice conversion apparatus, voice conversion learning apparatus, image generation apparatus, image generation learning apparatus, voice conversion method, voice conversion learning method, image generation method, image generation learning method, and computer program

Provided is a pharmaceutical composition containing 6-ethyl-3-{3-methoxy-4-[4-(4-methylpiperazin-1-yl)piperidin-1-yl]anilino}-5-[(oxan-4-yl)amino]pyrazine-2-carboxamide or a pharmaceutically acceptable salt thereof and a sweetener for reducing the bitterness of gilteritinib, suppressing the decrease in dissolution stability of gilteritinib due to, for example, stress such as heat and/or humidity over time, and having excellent dissolution stability. The pharmaceutical composition contains gilteritinib or a pharmaceutically acceptable salt thereof, a sweetener, and sugars and/or sugar alcohols.

Pharmaceutical composition

A conversion learning device includes: a source encoding unit that converts, by using a first machine learning model, a feature amount sequence of a source domain that is a characteristic of conversion-source content data, into a first internal representation vector sequence that is a matrix in which internal representation vectors at individual locations of the feature amount sequence of the source domain are arranged; a target encoding unit that converts, by using a second machine learning model, a feature amount sequence of a target domain that is a characteristic of conversion-target content data, into a second internal representation vector sequence that is a matrix in which internal representation vectors at individual locations of the feature amount sequence of the target domain are arranged; an attention matrix calculation unit that calculates, by using the first internal representation vector sequence and the second internal representation vector sequence, an attention matrix that is a matrix mapping the individual locations of the feature amount sequence of the source domain to the individual locations of the feature amount sequence of the target domain, and calculates a third internal representation vector sequence that is a product of an internal representation vector sequence calculated by linear conversion of the first internal representation vector sequence and the attention matrix; a target decoding unit that calculates, by using the third internal representation vector sequence, a feature amount sequence of a conversion domain that is used to convert the source domain into the conversion domain, by using a third machine learning model; and a learning execution unit that causes at least one of the target encoding unit and the target decoding unit to learn such that a distance between a submatrix of the feature amount sequence of the target domain and a submatrix of the feature amount sequence of the conversion domain becomes shorter.

Conversion learning apparatus, conversion learning method, conversion learning program and conversion apparatus

A parameter included in a fundamental frequency pattern of a voice can be estimated from the fundamental frequency pattern with high accuracy and the fundamental frequency pattern of the voice can be reconstructed from the parameter included in the fundamental frequency pattern. A learning unitlearns a deep generation model including an encoder which regards a parameter included in a fundamental frequency pattern in a voice signal as a latent variable of the deep generation model and estimates the latent variable from the fundamental frequency pattern in the voice signal on the basis of parallel data of the fundamental frequency pattern in the voice signal and the parameter included in the fundamental frequency pattern in the voice signal, and a decoder which reconstructs the fundamental frequency pattern in the voice signal from the latent variable.

Device, method, and program for analyzing speech signal

To be able to convert to a voice of the desired attribution. Learning an encoder for, on the basis of parallel data of a sound feature vector series in a conversion-source voice signal and a latent vector series in the conversion-source voice signal, and an attribution label indicating attribution of the conversion-source voice signal, estimating a latent vector series from input of a sound feature vector series and an attribution label, and a decoder for reconfiguring the sound feature vector series from input of the latent vector series and the attribution label.

Audio conversion learning device, audio conversion device, method, and program

The present invention relates to methods of converting a speech into another speech that sounds more natural. The method includes learning for a target conversion function and a target identifier according to an optimal condition in which the target conversion function and the target identifier compete with each other. The target conversion function converts source speech into target speech. The target identifier identifies whether the converted target speech follows the same distribution as actual target speech. The methods include learning for a source conversion function and a source identifier according to an optimal condition in which the source conversion function and the source identifier compete with each other. The source conversion function converts target speech into source speech, and the source identifier identifies whether the converted source speech follows the same distribution as actual source speech.

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Tokyo, Japan

Ko Tanaka