Abstract: The present disclosure relates to a medical image analysis method using a processor and a memory which are hardware. The method includes generating predicted second metadata for a medical image by using a prediction model, and determining a processing method of the medical image based on one of first metadata stored corresponding to the medical image and the second metadata.

Abstract: The inventive concept provides a teaching method for teaching a transfer position of a transfer robot. The teaching method includes: searching for an object on which a target object to be transferred by the transfer robot is placed, based on a 3D position information acquired by a first sensor; and acquiring coordinates of a second direction and coordinates of a third direction of the object based on a data acquired from a second sensor which is a different type from the first sensor.

Abstract: Provided is method of providing a conversation service in an electronic apparatus, including receiving a conversation input from a user terminal, identifying conversation history information associated with a user of the user terminal, obtaining score information corresponding to the user based on the conversation input and the conversation history information, obtaining response information based on the conversation input and the conversation history information, and providing the user terminal with at least one of the score information and the response information.

Abstract: An electrode insulating coating composition according to the present invention includes boehmite particles, a dispersant, a binder, and a solvent, wherein the dispersant includes two or more fatty acid compounds, and is included in an amount of 1.2 to 8.8 parts by weight with respect to 100 parts by weight of the boehmite particles.

Abstract: A graft copolymer, and a graft copolymer composition having excellent particulate dispersibility in a curable resin such as an epoxy resin and is applicable as a particulate impact reinforcing agent, a curable resin composition including same, and methods of preparing them.

Abstract: A recognition method includes extracting target data corresponding to a current window and padding data subsequent to the target data from sequence data; acquiring a state parameter corresponding to a previous window; and calculating a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data using a recurrent model.

Abstract: An electronic device and an method of the electronic device are provided, where the electronic device maintains a context that does not reflect a request for a secret conversation, in response to the request for the secret conversation being received from a first user, and generates a response signal to a voice signal of a second user based on the maintained context, in response to an end of the secret conversation with the first user.

Abstract: An apparatus and method for executing an application may execute a text string selected by a user or an application associated with a type of text string to input the text string to increase the user's convenience. The apparatus of executing an application includes a text string recognizer to determine a text string, a determiner to determine one or more candidate applications related to the text string, and an input location of the selected text string based on a type of the selected text string and the association model, an application list provider to generate and display a list of the candidate applications, and an application executer to execute a candidate application selected from the list and to input the selected text string into the input location of the candidate application.

Abstract: A speech recognition apparatus includes a probability calculator configured to calculate phoneme probabilities of an audio signal using an acoustic model; a candidate set extractor configured to extract a candidate set from a recognition target list; and a result returner configured to return a recognition result of the audio signal based on the calculated phoneme probabilities and the extracted candidate set.

Abstract: Provided is a processor-implemented method of generating a natural language, the method including generating a latent variable from an embedding vector that corresponds to an input utterance, determining attention information related to the input utterance by applying the generated latent variable to a neural network model, and outputting a natural language response that corresponds to the input utterance based on the calculated attention information.

Abstract: Disclosed is a speech recognition method and apparatus, wherein the apparatus acquires first outputs from sub-models in a recognition model based on a speech signal, acquires a second output including values corresponding to the sub-models from a classification model based on the speech signal, and recognizes the speech signal based on the first outputs and the second output.

Abstract: Provided is an automated interpretation method, apparatus, and system. The automated interpretation method includes encoding a voice signal in a first language to generate a first feature vector, decoding the first feature vector to generate a first language sentence in the first language, encoding the first language sentence to generate a second feature vector with respect to a second language, decoding the second feature vector to generate a second language sentence in the second language, controlling a generating of a candidate sentence list based on any one or any combination of the first feature vector, the first language sentence, the second feature vector, and the second language sentence, and selecting, from the candidate sentence list, a final second language sentence as a translation of the voice signal.

Abstract: A training method of an acoustic model includes constructing window-level input speech data based on a speech sequence; inputting the window-level input speech data to an acoustic model; calculating a sequence level-error based on an output of the acoustic model; acquiring window-level errors based on the sequence level-error; and updating the acoustic model based on the window-level errors.

Abstract: An electronic device and an method of the electronic device are provided, where the electronic device maintains a context that does not reflect a request for a secret conversation, in response to the request for the secret conversation being received from a first user, and generates a response signal to a voice signal of a second user based on the maintained context, in response to an end of the secret conversation with the first user.

Abstract: An out-of-service (OOS) sentence generating method includes: training models based on a target utterance template of a target service and a target sentence generated from the target utterance template; generating a similar utterance template that is similar to the target utterance template based on a trained model, among the trained models, and a sentence generated from an utterance template of another service; and generating a similar sentence that is similar to the target sentence based on another trained model, among the trained models, and the similar utterance template.

Abstract: An electronic device and an method of the electronic device are provided, where the electronic device maintains a context that does not reflect a request for a secret conversation, in response to the request for the secret conversation being received from a first user, and generates a response signal to a voice signal of a second user based on the maintained context, in response to an end of the secret conversation with the first user.

Abstract: An automated interpretation method includes: interpreting a source voice signal expressed in a first language by dividing the source voice signal into at least one word as a unit while the source voice signal is being input, and outputting, as an interpretation result in real time, a first target voice signal expressed in a second language by each unit; determining whether to re-output the interpretation result; and in response to a determination of the determining of whether to re-output the interpretation being a determination that the interpretation result is to be re-output, interpreting the source voice signal by a sentence as a unit and outputting, as the interpretation result, a second target voice signal expressed in the second language.

Abstract: A user adaptive speech recognition method and apparatus are provided. A speech recognition method includes extracting an identity vector representing an individual characteristic of a user from speech data, implementing a sub-neural network by inputting a sub-input vector including at least the identity vector to the sub-neural network, determining a scaling factor based on a result of the implementing of the sub-neural network, implementing a main neural network, configured to perform a recognition operation, by applying the determined scaling factor to the main neural network and inputting the speech data to the main neural network to which the determined scaling factor is applied, and indicating a recognition result of the implementation of the main neural network.

Abstract: An apparatus and method for web page access and an apparatus and method for structuring a web page access history are provided. The apparatus for structuring a web page access history includes a topic extractor configured to analyze a web page accessed by a user to extract at least one topic related to the page, a mapper configured to map the at least one extracted topic onto a node of an ontology-based data structure, and a summary graph generator configured to extract a sub graph including the mapped node from the data structure, and to generate a summary graph based on the extracted sub graph.

Abstract: A neural network method and apparatus, the method including providing a voice signal to a main neural network and a sub-neural network, obtaining a scaling factor by implementing the sub-neural network configured to generate the scaling factor by interpreting the provided voice signal, determining a size of a future context, based on the scaling factor, to be considered by the main neural network configured to perform speech recognition, obtaining a result of a recognizing of the voice signal by implementing the main neural network with the determined size of the future context.