Actionpower Corp.

Hwanbok Mun

Jihwa Lee

Dongchan Shin

Kangwook Kim

Seongmin Park

Seungho Kwak

Hyungwoo Kim

Subong Choi

Sangyoun Paik

Gyujin Kim

Disclosed is a method for determining important parts among a speech-to-text (STT) result and reference data, which is performed by a computing device. The method may include acquiring STT data generated by performing STT with respect to a speech signal; acquiring reference data; determining first important information in one data among the STT data and the reference data; and determining second important information linked with the first important information in other data different from data in which the first important information is determined among the STT data and the reference data.

Method for determining and linking important parts among STT result and reference data

Disclosed is a voice recognition method based on a neural network, performed by one or more processors of a computing device. The voice recognition method includes generating first text data based on a first language based on voice information. The method includes detecting problem text among the generated first text data. The method includes detecting a second language corresponding to the detected problem text. The method includes generating second text data based on the detected second language, in which the second language is different from the first language.

Method for recognizing the voice of audio containing foreign languages

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.

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Seoul, South Korea

Hwanbok Mun