Actionpower Corp.

Jihwa Lee

Dongchan Shin

Seongmin Park

Subong Choi

Hwanbok Mun

Sangyoun Paik

Kyung Ho Kim

Hwanseok Choi

Hongsik Jo

Jaeyup Song

Gyujin Kim

Yunseop Kim

Woochan Jeong

Alena Kazakova

Jinsuk Park

Disclosed is a computer program executable by one or more processors and stored in a computer-readable storage medium, the computer program causing the one or more processors to perform one or more operations below, the operations including: an operation of receiving first voice data from a first user terminal and receiving second voice data from a second user terminal; an operation of generating integrated voice data based on the first voice data and the second voice data; and an operation of generating the integrated voice data as a conversation record by using a voice recognition model.

Computing device for providing dialogues services

Disclosed is a method of editing a speech recognition result, the method being performed by a computing device. The method may include: displaying a word list satisfying a predetermined condition based on text information generated by speech recognition; determining a target word within the word list; and displaying a region corresponding to the target word within the text information, in which the predetermined condition includes at least one of predetermined word information for each user account and predetermined threshold information associated with a frequency of occurrence of a word.

Method of editing speech recognition result

Disclosed is a method of correcting text information. The method can be performed by a computing device. The method includes obtaining the text information. The method includes determining problem text within the text information. The method includes generating alternative text to correct the problem text by utilizing expanded text associated with the problem text or non-text type information associated with the problem text. The method includes providing information about the alternative text for correcting the problem text.

Method for editing text information

Disclosed is a method for detecting a voice from audio data, performed by a computing device according to an exemplary embodiment of the present disclosure. The method includes obtaining audio data; generating image data based on a spectrum of the obtained audio data; analyzing the generated image data by utilizing a pre-trained neural network model; and determining whether an automated response system (ARS) voice is included in the audio data, based on the analysis of the image data.

Method for detecting speech in audio data

Disclosed is a method of generating data based on input data by using a pre-trained artificial neural network model having an encoder-decoder structure. In particular, according to the present disclosure, a computing device generates new data based on a probability distribution of input data by using a pre-trained artificial neural network model having an encoder-decoder structure, and the pre-trained artificial neural network model having the encoder-decoder structure corresponds to a pre-trained model in which a latent vector layer is included between an encoder layer and a decoder layer of the artificial neural network model.

Method of generating data by using artificial neural network model having encoder-decoder structure

Disclosed is a method for error detection, performed by one or more processors of a computing device according to an example embodiment of the present disclosure. The method includes evaluating an error rate for a sentence to be evaluated, in a first language unit. the method includes evaluating an error rate in a second language unit which is smaller than the first language unit, based on the first language unit error.

Method for error detection by using top-down method

Disclosed is a method for speech recognition performed by one or more processors of a computing device. The method includes inputting voice information into an encoder to extract a first feature vector and calculating a first loss function. The method includes inputting the first feature vector extracted from the encoder to a first decoder to perform prediction on the voice information, calculating a second loss function, and extracting a second feature vector. The method includes inputting a second feature vector extracted from the first decoder to a second decoder to perform grapheme-based prediction, and calculating a third loss function. The method includes training at least one of the encoder, the first decoder, or the second decoder based on the first loss function, the second loss function, and the third loss function.

Method for speech recognition with grapheme information

Disclosed is a method for performing QA with multi-modal information. Specifically, according to the present disclosure, a computing device determines core text information from a video based on question data, determines core object information or core frame information from the video based on the core text information, and performs QA for the video based on the determined core text information, and the determined core object information or core frame information by utilizing a QA model.

Method for QA with multi-modal information

Disclosed is a method for generating data, the method is performed by one or more processors of a computing device. The method may include: segmenting text data generated based on speech information into a token unit; generating a first feature vector based on the text data segmented into the token unit, and generating a first label vector corresponding to the generated first feature vector, and generating a second feature vector and a second label vector by performing mix-up for each of the generated first feature vector and the generated first label vector.

Method for generating learning data for speech recognition error detection

Disclosed is a method for dialogued summarization with word graphs, which is performed by one or more processors of a computing device. The method may include: generating a word graph based on information on a dialogue which is a summary target; extracting at least one keyword based on the generated word graph; generating a plurality of candidate summary sentences based on the generated word graph; and calculating a score associated with at least one keyword for each of the plurality of candidate summary sentences, and selecting at least one of the plurality of candidate summary sentences based on the calculated score.

Growing community of inventors

Seoul, South Korea

Jihwa Lee