25 citations · 28 across the 4 of their papers we have counts for
4 papers
EM-Network: Oracle Guided Self-distillation for Sequence Learning
Ji Won Yoon, Sunghwan Ahn, Hyeonseung Lee +3
We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning. In contrast to con…
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition
Ji Won Yoon, Beom Jun Woo, Sunghwan Ahn +2
Recently, the advance in deep learning has brought a considerable improvement in the end-to-end speech recognition field, simplifying the traditional pipeline while producing promi…
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
Minchan Kim, Myeonghun Jeong, Byoung Jin Choi +3
Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect. In this paper, we propose a transfer learning framework fo…
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models
Ji Won Yoon, Hyung Yong Kim, Hyeonseung Lee +2
Knowledge distillation (KD), best known as an effective method for model compression, aims at transferring the knowledge of a bigger network (teacher) to a much smaller network (st…