Sequence-to-sequence korean phoneme-to-text conversion for korean speech recognition

In this paper, phoneme-to-text conversion based on sequence-to-sequence learning for speech recognition of Korean standard languages is proposed. To model the difference of Korean phonological change phenomenon, an RNN-LSTM network is used. 22.28% character error rate was achieved in an experiment with 2,200 Korean standard language sentences.