Actionpower Corp.

Seongmin Park

Jihwa Lee

Dongchan Shin

Seungho Kwak

Hwanbok Mun

Subong Choi

Sangyoun Paik

Alena Kazakova

Gyujin Kim

Disclosed are a method, a device, and a program for selecting a main speaker among speakers included in a sound source or a conversation record based on the sound source or the conversation record including conversation contents of at least one speaker and generating a summary based on the main speaker. A method of generating a summary for a sound source, the method being performed by at least one computing device, includes: generating a speak score for at least one speaker based on the sound source; determining a main speaker of the sound source based on a speak score for said at least one speaker; and generating a summary for the sound source in consideration of the determined main speaker.

Method of generating summary based on main speaker

Disclosed is a method for identifying a word corresponding to a target word in text information, which is performed by one or more processors of a computing device. The method may include: determining a target word; determining a threshold for an edit distance associated with the target word; determining a word of which the edit distance from the target word among words included in text information is equal to or less than the threshold; and identifying the word corresponding to the target word based on the determined word.

Method for identifying a word corresponding to a target word in text information

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 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 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.

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Seoul, South Korea

Seongmin Park