22 citations · 33 across the 9 of their papers we have counts for
10 papers
Configurable-Bandwidth Time-Frequency Modeling for Efficient Full-Band Speech Enhancement Across Sampling Rates
Ui-Hyeop Shin, Wooseok Kim, Hyung-Min Park
Speech enhancement systems are often developed for a fixed sampling rate, while time-frequency models become more expensive as the number of frequency bins increases. We propose TF…
DASH: Dual-View Self-Distillation with Multi-Layer Hidden Representations for Robust Speech Recognition
Jaeeun Baik, Ui-Hyeop Shin, Jiwoon Lee +2
Automatic Speech Recognition (ASR) often degrades in real-world noisy environments, making noise robustness essential for deployment. Supervised noise-augmented fine-tuning is a co…
Deep Filter Estimation from Inter-Frame Correlations for Monaural Speech Dereverberation
Ui-Hyeop Shin, Jun Hyung Kim, Jangyeon Kim +2
Speech dereverberation with a distant microphone is challenging because reverberation is correlated with the target speech, and models trained on simulated data often generalize po…
Asymmetric Encoder-Decoder Based on Time-Frequency Correlation for Speech Separation
Ui-Hyeop Shin, Hyung-Min Park
Speech separation in realistic acoustic environments remains challenging because overlapping speakers, background noise, and reverberation must be resolved simultaneously. Although…
TF-CorrNet: Leveraging Spatial Correlation for Continuous Speech Separation
Ui-Hyeop Shin, Bon Hyeok Ku, Hyung-Min Park
In general, multi-channel source separation has utilized inter-microphone phase differences (IPDs) concatenated with magnitude information in time-frequency domain, or real and ima…
Query-Based Asymmetric Modeling with Decoupled Input-Output Rates for Speech Restoration
Ui-Hyeop Shin, Jaehyun Ko, Woocheol Jeong +1
Speech restoration aims to recover clean speech from degraded recordings affected by noise, reverberation, bandwidth reduction, or other distortions, where input and output samplin…
Stack Less, Repeat More: A Block Reusing Approach for Progressive Speech Enhancement
Jangyeon Kim, Ui-Hyeop Shin, Jaehyun Ko +1
This paper presents an efficient speech enhancement (SE) approach that reuses a processing block repeatedly instead of conventional stacking. Rather than increasing the number of b…
Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation
Ui-Hyeop Shin, Sangyoun Lee, Taehan Kim +1
In speech separation, time-domain approaches have successfully replaced the time-frequency domain with latent sequence feature from a learnable encoder. Conventionally, the feature…
NeXt-TDNN: Modernizing Multi-Scale Temporal Convolution Backbone for Speaker Verification
Hyun-Jun Heo, Ui-Hyeop Shin, Ran Lee +2
In speaker verification, ECAPA-TDNN has shown remarkable improvement by utilizing one-dimensional(1D) Res2Net block and squeeze-and-excitation(SE) module, along with multi-layer fe…
Statistical Beamformer Exploiting Non-stationarity and Sparsity with Spatially Constrained ICA for Robust Speech Recognition
Ui-Hyeop Shin, Hyung-Min Park
In this paper, we present a statistical beamforming algorithm as a pre-processing step for robust automatic speech recognition (ASR). By modeling the target speech as a non-station…