Abstract: A language correction system, a method therefor, and a language correction model learning method of the system are disclosed. The system comprises a correction model learning unit and a language correction unit. The correction model learning unit performs machine learning on a plurality of data sets consisting of ungrammatical sentence data and error-free grammatical sentence data respectively corresponding to the ungrammatical sentence data, so as to generate a correction mode for detecting grammatical sentence data corresponding to ungrammatical sentence data to be corrected. The language correction unit generates, for a sentence to be corrected, a corresponding corrected sentence by using the correction model generated by the correction model learning unit, and displays and outputs the corrected parts together with the generated corrected sentence.

Abstract: A voice conversation reconstruction method performed by a voice conversation reconstruction apparatus is disclosed. The method includes acquiring speaker-specific voice recognition data about voice conversation, dividing the speaker-specific voice recognition data into a plurality of blocks using a boundary between tokens according to a predefined division criterion, arranging the plurality of blocks in chronological order irrespective of a speaker, merging blocks from continuous utterance of the same speaker among the arranged plurality of blocks, and reconstructing the plurality of blocks subjected to the merging in a conversation format in chronological order and based on a speaker.

Abstract: A language correction system, a method therefor, and a language correction model learning method of the system are disclosed. The system comprises a correction model learning unit and a language correction unit. The correction model learning unit performs machine learning on a plurality of data sets consisting of ungrammatical sentence data and error-free grammatical sentence data respectively corresponding to the ungrammatical sentence data, so as to generate a correction mode for detecting grammatical sentence data corresponding to ungrammatical sentence data to be corrected. The language correction unit generates, for a sentence to be corrected, a corresponding corrected sentence by using the correction model generated by the correction model learning unit, and displays and outputs the corrected parts together with the generated corrected sentence.

Abstract: A voice conversation reconstruction method performed by a voice conversation reconstruction apparatus is disclosed. The method includes acquiring speaker-specific voice recognition data about voice conversation, dividing the speaker-specific voice recognition data into a plurality of blocks using a boundary between tokens according to a predefined division criterion, arranging the plurality of blocks in chronological order irrespective of a speaker, merging blocks from continuous utterance of the same speaker among the arranged plurality of blocks, and reconstructing the plurality of blocks subjected to the merging in a conversation format in chronological order and based on a speaker.

Abstract: The voice recognition and translation method by a voice recognition system for multiple parties, according to one embodiment of the present invention, comprises the steps of: receiving a voice input; recognizing a first language which is the language of the voice; recognizing the text of the voice by means of a basic sound model on the basis of the first language; translating the recognized voice into an output language, if the first language is not the output language that has been preset as the output language; and storing and outputting the result in the output language.

Abstract: An artificial intelligence service method and a device therefor are disclosed. Specifically, a method for providing an artificial intelligence service by an artificial intelligence device may comprise the steps of: receiving an input of user data including user biometric information; performing user adaptive training for transforming a base artificial intelligence model to be suitable for characteristic of the user data; and providing an artificial intelligence service by applying an artificial intelligence model generated as a result of the user adaptive training.

Abstract: An automatic speech recognition device, according to the present invention, comprises: a memory for storing a program for converting speech data received via an interface module into transcription data, and outputting same; and a processor for executing the program stored in the memory, wherein, by executing the program, the processor converts the received speech data into pronunciation code data on the basis of a pre-trained first model, and converts the converted pronunciation code data into transcription data on the basis of a pre-trained second model.

Abstract: A translation method and an apparatus therefor are disclosed. Particularly, a method of performing sequence-to-sequence translation may include dividing an entire input into input units for each time point, the input units being units subjected to translation, inserting, into a corresponding one of the input units, a first symbol indicating a position of a symbol to be assigned a highest weight among symbols belonging to the corresponding input unit, and repeatedly deriving an output symbol from the input unit in which the first symbol is inserted each time the time point is increased.