Electronics and Telecommunications Research Institute

Yoonhyung Kim

Hoon Chung

Byung Ok Kang

Provided is a self-supervised learning method based on permutation invariant cross entropy. A self-supervised learning method based on permutation invariant cross entropy performed by an electronic device includes: defining a cross entropy loss function for pre-training of an end-to-end speech recognition model; configuring non-transcription speech corpus data composed only of speech as input data of the cross entropy loss function; setting all permutations of classes included in the non-transcription speech corpus data as an output target and calculating cross entropy losses for each class; and determining a minimum cross entropy loss among the calculated cross entropy losses for each class as a final loss.

Self-supervised learning method based on permutation invariant cross entropy and electronic device thereof

Disclosed herein are an apparatus and method for audio-video sampling frequency ratio unification, including memory configured to store at least one program, and a processor configured to execute the program, wherein the program is configured to perform receiving an audio signal and a video signal, adjusting a ratio of a sampling frequency of the audio signal to a sampling frequency of the video signal so that the sampling frequency ratio is constant based on a deep learning network, and outputting an adjusted audio signal and the video signal.

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Daejeon, South Korea

Yoonhyung Kim