Search Results for author: Sunghwan Ahn

Found 5 papers, 1 papers with code

HILCodec: High Fidelity and Lightweight Neural Audio Codec

1 code implementation8 May 2024 Sunghwan Ahn, Beom Jun Woo, Min Hyun Han, Chanyeong Moon, Nam Soo Kim

The recent advancement of end-to-end neural audio codecs enables compressing audio at very low bitrates while reconstructing the output audio with high fidelity.

EM-Network: Oracle Guided Self-distillation for Sequence Learning

no code implementations14 Jun 2023 Ji Won Yoon, Sunghwan Ahn, Hyeonseung Lee, Minchan Kim, Seok Min Kim, Nam Soo Kim

We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning.

Decoder Machine Translation +2

Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition

no code implementations28 Nov 2022 Ji Won Yoon, Beom Jun Woo, Sunghwan Ahn, Hyeonseung Lee, Nam Soo Kim

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 promising results.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models

no code implementations5 Nov 2021 Ji Won Yoon, Hyung Yong Kim, Hyeonseung Lee, Sunghwan Ahn, Nam Soo Kim

Extending this supervised scheme further, we introduce a new type of teacher model for connectionist temporal classification (CTC)-based sequence models, namely Oracle Teacher, that leverages both the source inputs and the output labels as the teacher model's input.

Knowledge Distillation Machine Translation +5

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