Kabushiki Kaisha Toshiba

Ziosoft, Incorporated

Yu Nasu

Masahiro Morita

Masatsune Tamura

Yamato Ohtani

Ryo Morinaka

A medical image processing apparatus includes an acquisition unit and a processing unit. The acquisition unit acquires volume data of a subject. The processing unit displays a three-dimensional image by rendering the acquired volume data, on a display unit. The processing unit displays a first object showing (i) a point on a body surface of the subject and (ii) a direction to the volume data in the three-dimensional image, and displays a two-dimensional image of a surface including the point on the body surface and being defined based on the direction, in the volume data. The processing unit acquires information of a first operation to change display of the two-dimensional image, and moves the point on the body surface along the body surface of the subject based on the first operation to update display of the first object and the two-dimensional image.

Medical image processing apparatus, medical image processing method and medical image processing system

According to an embodiment, a training device trains a neural network that outputs a posterior probability that an input signal belongs to a particular class. An output layer of the neural network includes N units respectively corresponding to classes and one additional unit. The training device includes a propagator, a probability calculator, and an updater. The propagator supplies a sample signal to the neural network and acquires (N+1) input values for each unit at the output layer. The probability calculator supplies the input values to a function to generate a probability vector including (N+1) probability values respectively corresponding to the units at the output layer. The updater updates a parameter included in the neural network in such a manner to reduce an error between a teacher vector including (N+1) target values and the probability vector. A target value corresponding to the additional unit is a predetermined constant value.

Training device, speech detection device, training method, and computer program product

According to an embodiment, a detection apparatus detects a partial series similar to a search pattern from a parameter series including a sequence of parameters. The apparatus includes a local score acquirer, a difference score calculator, an accumulative score calculator, and a determiner. The local score acquirer is configured to acquire a local score representing a likelihood of the parameter in the search pattern for each of the parameters. The difference score calculator is configured to calculate a difference score by subtracting a threshold from the local score for each of the parameters. The accumulative score calculator is configured to calculate an accumulative score by accumulating the difference scores. The determiner is configured to compare the accumulative score with a reference value in size to determine whether the partial series is similar to the search pattern.

Detection apparatus, detection method, and computer program product

According to an embodiment, a voice processing device includes an interface system, a determining processor, and a predicting processor. The interface system configured to receive neutral voice data representing audio in a neutral voice of a user. The determining processor configured to determine a predictive parameter based at least in part on the neutral voice data. The predicting processor configured to predict a voice conversion model for converting the neutral voice of the speaker to a target voice using at least the predictive parameter.

Device for predicting voice conversion model, method of predicting voice conversion model, and computer program product

According to an embodiment, a text-to-speech device includes a context acquirer, an acoustic model parameter acquirer, a conversion parameter acquirer, a converter, and a waveform generator. The context acquirer is configured to acquire a context sequence affecting fluctuations in voice. The acoustic model parameter acquirer is configured to acquire an acoustic model parameter sequence that corresponds to the context sequence and represents an acoustic model in a standard speaking style of a target speaker. The conversion parameter acquirer is configured to acquire a conversion parameter sequence corresponding to the context sequence to convert an acoustic model parameter in the standard speaking style into one in a different speaking style. The converter is configured to convert the acoustic model parameter sequence using the conversion parameter sequence. The waveform generator is configured to generate a voice signal based on the acoustic model parameter sequence acquired after conversion.

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Tokyo, Japan

Yu Nasu