56 citations · 166 across the 19 of their papers we have counts for
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eess.AS2019★ 56 cited
Time-Domain Multi-modal Bone/air Conducted Speech Enhancement
Cheng Yu, Kuo-Hsuan Hung, Syu-Siang Wang +3
Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE). However, video clips usua…
eess.AS2019★ 25 cited
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques
Jyun-Yi Wu, Cheng Yu, Szu-Wei Fu +3
Most recent studies on deep learning based speech enhancement (SE) focused on improving denoising performance. However, successful SE applications require striking a desirable bala…